Setup and Packages

knitr::opts_chunk$set(tidy=TRUE, tidy.opts=list(width.cutoff=20))
library(tidyverse)
library(janitor)
library(skimr)
library(broom)
library(car)
library(lmtest)

Loading the files

# getwd()
hdi <- read.csv("data/hdi_human_development_index.csv")
broadband <- read.csv("data/broadband_subscribers_per_100_people.csv")
gdp <- read.csv("data/gdp_pcap.csv")

glimpse(hdi)
## Rows: 193
## Columns: 35
## $ country <chr> "Afghanistan", "Angola", "Albania", "Andorra", "UAE", "Argenti…
## $ X1990   <dbl> 0.285, NA, 0.654, NA, 0.713, 0.733, 0.663, NA, 0.867, 0.832, N…
## $ X1991   <dbl> 0.291, NA, 0.638, NA, 0.724, 0.739, 0.652, NA, 0.868, 0.835, N…
## $ X1992   <dbl> 0.301, NA, 0.622, NA, 0.729, 0.743, 0.618, NA, 0.868, 0.841, N…
## $ X1993   <dbl> 0.311, NA, 0.624, NA, 0.736, 0.748, 0.621, NA, 0.872, 0.845, N…
## $ X1994   <dbl> 0.305, NA, 0.629, NA, 0.742, 0.753, 0.625, NA, 0.873, 0.851, N…
## $ X1995   <dbl> 0.329, NA, 0.638, NA, 0.747, 0.754, 0.631, NA, 0.882, 0.856, 0…
## $ X1996   <dbl> 0.334, NA, 0.647, NA, 0.753, 0.760, 0.635, NA, 0.884, 0.861, 0…
## $ X1997   <dbl> 0.338, NA, 0.645, NA, 0.761, 0.768, 0.644, NA, 0.887, 0.865, 0…
## $ X1998   <dbl> 0.338, NA, 0.659, NA, 0.770, 0.772, 0.655, NA, 0.891, 0.870, 0…
## $ X1999   <dbl> 0.347, 0.379, 0.671, NA, 0.780, 0.783, 0.661, NA, 0.893, 0.874…
## $ X2000   <dbl> 0.351, 0.391, 0.682, 0.825, 0.790, 0.789, 0.667, NA, 0.897, 0.…
## $ X2001   <dbl> 0.355, 0.401, 0.691, 0.832, 0.798, 0.795, 0.672, NA, 0.900, 0.…
## $ X2002   <dbl> 0.383, 0.417, 0.697, 0.838, 0.805, 0.795, 0.682, NA, 0.902, 0.…
## $ X2003   <dbl> 0.392, 0.435, 0.705, 0.840, 0.810, 0.804, 0.692, NA, 0.906, 0.…
## $ X2004   <dbl> 0.408, 0.448, 0.710, 0.849, 0.815, 0.808, 0.700, NA, 0.910, 0.…
## $ X2005   <dbl> 0.417, 0.463, 0.723, 0.842, 0.819, 0.812, 0.711, NA, 0.914, 0.…
## $ X2006   <dbl> 0.426, 0.475, 0.732, 0.851, 0.823, 0.822, 0.725, NA, 0.917, 0.…
## $ X2007   <dbl> 0.442, 0.492, 0.742, 0.859, 0.827, 0.825, 0.739, 0.827, 0.919,…
## $ X2008   <dbl> 0.446, 0.504, 0.749, 0.864, 0.831, 0.833, 0.743, 0.830, 0.924,…
## $ X2009   <dbl> 0.458, 0.517, 0.756, 0.866, 0.832, 0.835, 0.741, 0.827, 0.927,…
## $ X2010   <dbl> 0.465, 0.528, 0.769, 0.870, 0.835, 0.844, 0.747, 0.828, 0.929,…
## $ X2011   <dbl> 0.474, 0.545, 0.781, 0.875, 0.839, 0.852, 0.752, 0.828, 0.933,…
## $ X2012   <dbl> 0.484, 0.557, 0.790, 0.876, 0.843, 0.850, 0.761, 0.833, 0.938,…
## $ X2013   <dbl> 0.492, 0.567, 0.793, 0.862, 0.846, 0.852, 0.766, 0.835, 0.935,…
## $ X2014   <dbl> 0.497, 0.577, 0.797, 0.866, 0.853, 0.855, 0.772, 0.837, 0.937,…
## $ X2015   <dbl> 0.496, 0.603, 0.797, 0.869, 0.857, 0.859, 0.777, 0.839, 0.938,…
## $ X2016   <dbl> 0.495, 0.609, 0.797, 0.872, 0.861, 0.857, 0.783, 0.842, 0.939,…
## $ X2017   <dbl> 0.496, 0.610, 0.798, 0.873, 0.884, 0.861, 0.785, 0.845, 0.940,…
## $ X2018   <dbl> 0.498, 0.611, 0.801, 0.875, 0.901, 0.861, 0.789, 0.850, 0.945,…
## $ X2019   <dbl> 0.507, 0.611, 0.805, 0.876, 0.915, 0.861, 0.796, 0.851, 0.947,…
## $ X2020   <dbl> 0.501, 0.610, 0.794, 0.851, 0.909, 0.851, 0.761, 0.840, 0.950,…
## $ X2021   <dbl> 0.486, 0.609, 0.794, 0.871, 0.903, 0.847, 0.786, 0.843, 0.954,…
## $ X2022   <dbl> 0.495, 0.615, 0.806, 0.893, 0.921, 0.858, 0.801, 0.848, 0.952,…
## $ X2023   <dbl> 0.496, 0.616, 0.810, 0.913, 0.940, 0.865, 0.811, 0.851, 0.958,…
glimpse(broadband)
## Rows: 206
## Columns: 27
## $ country <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "UAE",…
## $ X1998   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.10700, N…
## $ X1999   <dbl> NA, NA, NA, NA, NA, 0.01180, NA, NA, NA, NA, 0.63700, NA, NA, …
## $ X2000   <dbl> NA, NA, NA, NA, NA, 0.05650, NA, NA, NA, NA, 2.38000, NA, NA, …
## $ X2001   <dbl> NA, NA, NA, NA, NA, 0.22500, 0.25000, 0.00019, NA, 0.63400, 3.…
## $ X2002   <dbl> NA, NA, NA, NA, 1.73000, 0.42000, 0.38900, 0.00026, NA, 1.3200…
## $ X2003   <chr> "1.52", "", "", "", "5.18", "0.723", "0.674", "0.00033", "", "…
## $ X2004   <chr> "7.47", "0.00085", "", "", "8.45", "1.27", "1.4", "0.0331", "2…
## $ X2005   <chr> "13", "0.0009", "", "0.00884", "13.4", "2.77", "2.36", "0.0662…
## $ X2006   <chr> "14.6", "0.00197", "0.0373", "", "18.4", "4.8", "4", "", "1.91…
## $ X2007   <dbl> 16.60000, 0.00193, 0.05630, 0.33100, 22.60000, 6.75000, 6.5000…
## $ X2008   <dbl> 18.80000, 0.00189, 0.07390, 2.14000, 24.80000, 8.85000, 7.7100…
## $ X2009   <dbl> 19.00000, 0.00364, 0.06690, 3.11000, 27.30000, 10.30000, 8.590…
## $ X2010   <chr> "19.2", "0.0053", "0.0644", "3.6", "30.4", "11.3", "9.76", "3.…
## $ X2011   <chr> "", "", "0.0653", "4.4", "33.1", "12", "11", "5.49", "6.95", "…
## $ X2012   <dbl> NA, 0.00491, 0.08150, 5.50000, 35.00000, 12.70000, 12.20000, 7…
## $ X2013   <dbl> 18.70000, 0.00474, 0.08520, 6.28000, 36.90000, 13.30000, 14.70…
## $ X2014   <dbl> 18.60000, 0.00457, 0.32300, 7.16000, 39.00000, 13.30000, 15.20…
## $ X2015   <dbl> 18.2000, 0.0209, 0.5450, 8.3800, 42.5000, 14.2000, 15.8000, 9.…
## $ X2016   <dbl> NA, 0.0254, 0.2900, 9.1900, 45.0000, 14.4000, 16.5000, 10.2000…
## $ X2017   <dbl> NA, 0.0257, 0.3210, 10.5000, 46.5000, 29.9000, 17.7000, 10.800…
## $ X2018   <dbl> NA, 0.0435, 0.3500, 12.5000, 47.4000, 32.4000, 19.0000, 11.900…
## $ X2019   <dbl> 17.7000, 0.0520, 0.3680, 15.1000, 47.5000, 32.5000, 19.6000, 1…
## $ X2020   <dbl> 17.7000, 0.0680, 0.3630, 17.7000, 48.7000, 34.3000, 21.2000, 1…
## $ X2021   <dbl> 17.7000, 0.0664, 0.3910, 19.6000, 50.3000, 36.5000, 23.1000, 1…
## $ X2022   <dbl> 17.5000, 0.0796, 0.3870, 20.7000, 51.2000, 36.8000, 24.7000, 1…
## $ X2023   <dbl> NA, 0.0801, 0.3740, 22.5000, 51.7000, 37.1000, 25.4000, 18.500…
glimpse(gdp)
## Rows: 193
## Columns: 303
## $ geo   <chr> "afg", "ago", "alb", "and", "are", "arg", "arm", "atg", "aus", "…
## $ name  <chr> "Afghanistan", "Angola", "Albania", "Andorra", "UAE", "Argentina…
## $ X1800 <dbl> 480.7548, 373.0514, 469.3082, 1370.1511, 1142.3649, 1698.6668, 5…
## $ X1801 <dbl> 480.7548, 374.2587, 470.6541, 1372.4404, 1145.7988, 1704.5903, 5…
## $ X1802 <dbl> 480.7548, 376.0697, 472.0039, 1374.7297, 1150.3774, 1710.5345, 5…
## $ X1803 <dbl> 480.7548, 377.8806, 473.3575, 1378.1636, 1154.9561, 1716.4993, 5…
## $ X1804 <dbl> 480.7548, 379.0879, 474.7151, 1380.4529, 1159.5347, 1722.4850, 5…
## $ X1805 <dbl> 480.7548, 380.8988, 476.0765, 1382.7423, 1162.9687, 1728.4916, 5…
## $ X1806 <dbl> 480.7548, 382.7097, 477.4418, 1385.0316, 1167.5473, 1734.5191, 5…
## $ X1807 <dbl> 480.7548, 384.5207, 478.8110, 1387.3209, 1172.1259, 1740.5676, 5…
## $ X1808 <dbl> 480.7548, 386.3316, 480.1842, 1390.7548, 1176.7045, 1746.6372, 6…
## $ X1809 <dbl> 480.7548, 387.5389, 481.5613, 1393.0441, 1181.2831, 1752.7280, 6…
## $ X1810 <dbl> 480.7548, 389.3498, 482.9423, 1395.3335, 1185.8617, 1758.8400, 6…
## $ X1811 <dbl> 480.7548, 391.1607, 484.3273, 1397.6228, 1189.2957, 1764.9734, 6…
## $ X1812 <dbl> 480.7548, 392.9717, 485.7163, 1401.0567, 1193.8743, 1771.1281, 6…
## $ X1813 <dbl> 480.7548, 394.7826, 487.1093, 1403.3460, 1198.4529, 1777.3043, 6…
## $ X1814 <dbl> 480.7548, 396.5935, 488.5062, 1405.6353, 1203.0315, 1783.5020, 6…
## $ X1815 <dbl> 480.7548, 398.4044, 489.9072, 1407.9246, 1207.6102, 1789.7213, 6…
## $ X1816 <dbl> 480.7548, 399.6117, 491.3122, 1411.3586, 1212.1888, 1795.9623, 6…
## $ X1817 <dbl> 480.7548, 401.4227, 492.7212, 1413.6479, 1216.7674, 1802.2251, 6…
## $ X1818 <dbl> 480.7548, 403.2336, 494.1343, 1415.9372, 1221.3460, 1808.5097, 6…
## $ X1819 <dbl> 480.7548, 405.0445, 495.5514, 1418.2265, 1225.9246, 1814.8163, 6…
## $ X1820 <dbl> 480.7548, 406.8555, 496.9725, 1421.6605, 1230.5032, 1821.1448, 6…
## $ X1821 <dbl> 480.7548, 408.6664, 498.3978, 1437.6856, 1235.0819, 1834.9024, 6…
## $ X1822 <dbl> 481.8440, 410.4773, 499.8271, 1454.8555, 1239.6605, 1848.7640, 6…
## $ X1823 <dbl> 482.9358, 412.2882, 501.2605, 1472.0253, 1244.2391, 1862.7303, 6…
## $ X1824 <dbl> 484.0301, 414.0992, 502.6981, 1490.3397, 1248.8177, 1876.8021, 6…
## $ X1825 <dbl> 485.1268, 415.9101, 504.1397, 1507.5096, 1253.3963, 1890.9802, 6…
## $ X1826 <dbl> 486.2260, 417.7210, 505.5856, 1525.8240, 1257.9750, 1905.2654, 6…
## $ X1827 <dbl> 487.3277, 419.5320, 507.0355, 1544.1385, 1262.5536, 1919.6585, 6…
## $ X1828 <dbl> 488.4319, 421.3429, 508.4896, 1562.4530, 1268.2768, 1934.1603, 6…
## $ X1829 <dbl> 489.5386, 423.1538, 509.9479, 1580.7674, 1272.8555, 1948.7717, 6…
## $ X1830 <dbl> 490.6478, 424.9647, 511.4103, 1599.0819, 1277.4341, 1963.4935, 6…
## $ X1831 <dbl> 491.7595, 427.3793, 512.8770, 1618.5410, 1282.0127, 1978.3265, 6…
## $ X1832 <dbl> 492.8737, 429.1902, 514.3478, 1638.0001, 1286.5913, 1993.2715, 6…
## $ X1833 <dbl> 493.9905, 431.0012, 515.8229, 1657.4593, 1292.3146, 2008.3295, 6…
## $ X1834 <dbl> 495.1098, 432.8121, 517.3022, 1676.9184, 1296.8932, 2023.5012, 7…
## $ X1835 <dbl> 496.2316, 434.6230, 518.7858, 1696.3775, 1301.4718, 2038.7875, 7…
## $ X1836 <dbl> 497.3559, 436.4340, 520.2736, 1716.9813, 1306.0504, 2054.1893, 7…
## $ X1837 <dbl> 498.4828, 438.2449, 521.7656, 1737.5850, 1311.7737, 2069.7075, 7…
## $ X1838 <dbl> 499.6123, 440.6595, 523.2620, 1758.1888, 1316.3523, 2085.3428, 7…
## $ X1839 <dbl> 500.7443, 442.4704, 524.7626, 1778.7926, 1320.9309, 2101.0963, 7…
## $ X1840 <dbl> 501.8789, 444.2813, 526.2676, 1800.5410, 1325.5095, 2116.9688, 7…
## $ X1841 <dbl> 503.0161, 446.0922, 527.7768, 1821.1448, 1331.2328, 2132.9612, 7…
## $ X1842 <dbl> 504.1558, 448.5068, 529.2904, 1842.8932, 1335.8114, 2149.0745, 7…
## $ X1843 <dbl> 505.2982, 450.3177, 530.8083, 1864.6417, 1341.5347, 2165.3094, 7…
## $ X1844 <dbl> 506.4431, 452.1287, 532.3306, 1887.5348, 1346.1133, 2181.6670, 7…
## $ X1845 <dbl> 507.5906, 453.9396, 533.8573, 1909.2832, 1350.6919, 2198.1481, 7…
## $ X1846 <dbl> 508.7407, 456.3542, 535.3883, 1932.1763, 1356.4152, 2214.7538, 7…
## $ X1847 <dbl> 509.8934, 458.1651, 536.9237, 1955.0693, 1360.9938, 2231.4849, 7…
## $ X1848 <dbl> 511.0487, 459.9760, 538.4635, 1977.9624, 1366.7171, 2248.3424, 7…
## $ X1849 <dbl> 512.2066, 462.3906, 540.0078, 2002.0002, 1371.2957, 2265.3273, 7…
## $ X1850 <dbl> 513.3672, 464.2015, 541.5564, 2026.0379, 1377.0190, 2282.4404, 8…
## $ X1851 <dbl> 514.5304, 466.6161, 543.1095, 2050.0756, 1381.5976, 2300.7653, 8…
## $ X1852 <dbl> 515.6962, 468.4271, 544.6671, 2074.1134, 1387.3209, 2319.2372, 8…
## $ X1853 <dbl> 516.8647, 470.2380, 546.2291, 2099.2958, 1391.8995, 2337.8575, 8…
## $ X1854 <dbl> 518.0358, 472.6526, 547.7956, 2123.3335, 1397.6228, 2356.6272, 8…
## $ X1855 <dbl> 519.2096, 474.4635, 549.3666, 2148.5159, 1402.2014, 2375.5477, 8…
## $ X1856 <dbl> 520.3860, 476.8780, 550.9421, 2174.8430, 1407.9246, 2394.6200, 8…
## $ X1857 <dbl> 521.5651, 478.6890, 552.5222, 2200.0253, 1412.5033, 2413.8455, 8…
## $ X1858 <dbl> 522.7468, 481.1035, 554.1067, 2226.3524, 1418.2265, 2433.2253, 8…
## $ X1859 <dbl> 523.9313, 482.9145, 555.6958, 2252.6794, 1423.9498, 2452.7608, 8…
## $ X1860 <dbl> 525.1184, 485.3290, 557.2895, 2280.1511, 1428.5284, 2472.4530, 8…
## $ X1861 <dbl> 526.3082, 487.1400, 558.8877, 2306.4782, 1434.2517, 2492.3226, 8…
## $ X1862 <dbl> 527.5008, 489.5546, 560.4905, 2333.9499, 1439.9750, 2512.3519, 8…
## $ X1863 <dbl> 528.6960, 491.3655, 562.0979, 2362.5662, 1444.5536, 2532.5422, 8…
## $ X1864 <dbl> 529.8939, 493.7801, 563.7099, 2390.0379, 1450.2768, 2552.8947, 9…
## $ X1865 <dbl> 531.0945, 496.1946, 565.3266, 2418.6543, 1456.0001, 2573.4107, 9…
## $ X1866 <dbl> 532.2979, 498.0056, 566.9478, 2447.2706, 1461.7234, 2594.0916, 9…
## $ X1867 <dbl> 533.5040, 500.4201, 568.5738, 2477.0316, 1466.3020, 2614.9388, 9…
## $ X1868 <dbl> 534.7128, 502.2311, 570.2043, 2505.6480, 1472.0253, 2635.9534, 9…
## $ X1869 <dbl> 535.9243, 504.6456, 571.8396, 2535.4090, 1477.7486, 2657.1370, 9…
## $ X1870 <dbl> 537.1386, 507.0602, 573.4796, 2566.3147, 1483.4718, 2678.4908, 9…
## $ X1871 <dbl> 538.3557, 509.4748, 575.1242, 2596.0757, 1505.2203, 2736.7925, 9…
## $ X1872 <dbl> 539.5755, 511.2857, 576.7736, 2626.9814, 1526.9687, 2796.3632, 9…
## $ X1873 <dbl> 540.7981, 513.7003, 578.4277, 2657.8870, 1548.7171, 2857.2306, 9…
## $ X1874 <dbl> 542.0234, 516.1149, 580.0865, 2689.9373, 1571.6102, 2919.4228, 9…
## $ X1875 <dbl> 543.2515, 517.9258, 581.7501, 2721.9877, 1594.5033, 2982.9688, 1…
## $ X1876 <dbl> 544.4824, 520.3403, 583.4185, 2754.0380, 1618.5410, 2988.6921, 1…
## $ X1877 <dbl> 545.7161, 522.7549, 585.0917, 2787.2329, 1641.4341, 3227.9248, 1…
## $ X1878 <dbl> 546.9526, 525.1695, 586.7696, 2820.4279, 1665.4718, 2997.8493, 1…
## $ X1879 <dbl> 548.1919, 527.5841, 588.4524, 2853.6229, 1689.5096, 3049.3587, 1…
## $ X1880 <dbl> 549.4340, 529.9986, 590.1400, 2887.9625, 1714.6920, 2926.8807, 1…
## $ X1881 <dbl> 549.4340, 531.8096, 591.8324, 2922.3021, 1739.8744, 2908.5663, 1…
## $ X1882 <dbl> 556.3763, 534.2242, 593.5297, 2956.6417, 1765.0567, 3557.5852, 1…
## $ X1883 <dbl> 563.4063, 536.6387, 595.2319, 2992.1260, 1790.2391, 3860.9186, 1…
## $ X1884 <dbl> 570.5252, 539.0533, 596.9389, 3027.6103, 1816.5662, 3990.2645, 1…
## $ X1885 <dbl> 577.7340, 541.4679, 598.6509, 3063.0946, 1842.8932, 4468.7299, 1…
## $ X1886 <dbl> 585.0339, 543.8824, 600.3677, 3099.7235, 1870.3649, 4316.4909, 1…
## $ X1887 <dbl> 592.4260, 546.2970, 602.0895, 3136.3525, 1897.8366, 4396.6167, 1…
## $ X1888 <dbl> 599.9115, 548.7116, 603.8162, 3174.1260, 1925.3083, 4829.2960, 1…
## $ X1889 <dbl> 607.4916, 551.1262, 605.5478, 3211.8996, 1952.7800, 4908.2771, 1…
## $ X1890 <dbl> 615.1675, 553.5407, 607.2845, 3249.6732, 1981.3964, 4408.0633, 1…
## $ X1891 <dbl> 622.9404, 555.9553, 609.0261, 3288.5915, 2010.0127, 4156.2393, 1…
## $ X1892 <dbl> 630.8115, 558.3699, 610.7727, 3327.5097, 2039.7738, 4865.9249, 1…
## $ X1893 <dbl> 638.7820, 560.7844, 612.5243, 3367.5726, 2069.5348, 5046.7803, 1…
## $ X1894 <dbl> 646.8533, 563.1990, 614.2809, 3407.6355, 2099.2958, 5686.6420, 1…
## $ X1895 <dbl> 655.0265, 565.6136, 616.0426, 3447.6984, 2130.2014, 6162.8181, 1…
## $ X1896 <dbl> 663.3030, 568.0282, 617.8093, 3488.9059, 2161.1071, 6542.8433, 1…
## $ X1897 <dbl> 671.6841, 571.0464, 619.5811, 3530.1135, 2193.1574, 5150.9438, 1…
## $ X1898 <dbl> 680.1710, 573.4610, 621.3579, 3572.4657, 2225.2077, 5426.8055, 1…
## $ X1899 <dbl> 688.7653, 575.8755, 623.1399, 3614.8179, 2257.2580, 6194.8684, 1…
## $ X1900 <dbl> 697.4681, 578.2901, 624.9270, 3657.1701, 2290.4530, 5245.9501, 1…
## $ X1901 <dbl> 706.2808, 580.7047, 626.7192, 3700.6670, 2323.6480, 5255.1074, 1…
## $ X1902 <dbl> 715.2050, 583.7229, 628.5165, 3745.3085, 2357.9876, 4957.4973, 1…
## $ X1903 <dbl> 724.2418, 586.1374, 630.3190, 3788.8053, 2392.3272, 5458.8558, 1…
## $ X1904 <dbl> 733.3929, 588.5520, 632.1267, 3834.5915, 2426.6669, 5821.7112, 1…
## $ X1905 <dbl> 742.6596, 590.9666, 633.9395, 3880.3777, 2462.1512, 6347.1074, 1…
## $ X1906 <dbl> 752.0434, 593.9848, 635.7576, 3926.1638, 2497.6354, 6419.2207, 1…
## $ X1907 <dbl> 761.5457, 596.3994, 637.5809, 3973.0947, 2534.2644, 6311.6232, 1…
## $ X1908 <dbl> 771.1681, 598.8140, 639.4093, 4020.0255, 2572.0379, 6672.1892, 1…
## $ X1909 <dbl> 780.9121, 601.8322, 641.2431, 4068.1010, 2608.6669, 6748.8811, 1…
## $ X1910 <dbl> 790.7793, 604.2467, 643.0821, 4116.1764, 2647.5851, 6973.2333, 1…
## $ X1911 <dbl> 800.7710, 607.2650, 644.9263, 4165.3966, 2685.3587, 6834.7301, 1…
## $ X1912 <dbl> 810.8891, 609.6795, 646.7759, 4214.6167, 2724.2769, 7123.1830, 1…
## $ X1913 <dbl> 821.1350, 612.0941, 648.6308, 4264.9815, 2764.3398, 6927.4471, 1…
## $ X1914 <dbl> 831.5103, 630.8070, 667.9330, 4335.9500, 2630.4153, 6024.3150, 1…
## $ X1915 <dbl> 842.0168, 649.5200, 687.8098, 4409.2079, 2503.3587, 5919.0068, 1…
## $ X1916 <dbl> 852.6560, 668.8366, 708.2780, 4482.4658, 2383.1700, 5639.7112, 1…
## $ X1917 <dbl> 863.4296, 688.7568, 729.3553, 4558.0130, 2267.5599, 5090.2771, 1…
## $ X1918 <dbl> 874.3393, 708.6770, 751.0598, 4634.7048, 2157.6731, 5925.8747, 1…
## $ X1919 <dbl> 885.3869, 729.8045, 773.4103, 4712.5413, 2273.2832, 6033.4722, 1…
## $ X1920 <dbl> 896.5742, 751.5357, 796.4258, 4791.5224, 2394.6166, 6336.8056, 1…
## $ X1921 <dbl> 907.9027, 775.6814, 820.1263, 4873.9375, 2522.8178, 6333.3716, 9…
## $ X1922 <dbl> 919.3744, 800.4307, 844.5321, 4958.6419, 2656.7424, 6634.4156, 1…
## $ X1923 <dbl> 930.9910, 825.7838, 869.6641, 5044.4910, 2638.4279, 7111.7364, 1…
## $ X1924 <dbl> 942.7545, 852.3441, 895.5440, 5131.4847, 2620.1134, 7399.0446, 1…
## $ X1925 <dbl> 954.6665, 879.5080, 922.1941, 5219.6231, 2601.7990, 7150.6547, 1…
## $ X1926 <dbl> 966.7291, 907.2756, 949.6372, 5310.0508, 2544.5662, 7286.8685, 1…
## $ X1927 <dbl> 978.9440, 936.2505, 977.8971, 5401.6231, 2489.6229, 7583.3340, 1…
## $ X1928 <dbl> 991.3134, 965.8290, 1006.9978, 5494.3401, 2435.8241, 7829.4346, …
## $ X1929 <dbl> 1003.8390, 996.6148, 1036.9646, 5589.3464, 2258.4027, 7967.9378,…
## $ X1930 <dbl> 1016.5229, 1028.6078, 1067.8232, 5685.4973, 2094.7171, 7443.6862…
## $ X1931 <dbl> 1029.3670, 1061.2046, 1099.6000, 5782.7929, 1942.4781, 6772.9188…
## $ X1932 <dbl> 1042.3735, 1094.4049, 1132.3225, 5882.3778, 1801.6857, 6426.0886…
## $ X1933 <dbl> 1055.5442, 1129.4162, 1166.0188, 5984.2521, 1671.1951, 6606.9439…
## $ X1934 <dbl> 1068.8814, 1165.0312, 1200.7178, 6087.2709, 1549.8618, 7015.5855…
## $ X1935 <dbl> 1082.3871, 1202.4571, 1236.4494, 6192.5791, 1525.8240, 7206.7427…
## $ X1936 <dbl> 1096.0635, 1240.4866, 1273.2443, 6299.0320, 1502.9309, 7138.0635…
## $ X1937 <dbl> 1109.9127, 1279.7234, 1311.1342, 6406.6295, 1480.0379, 7526.1012…
## $ X1938 <dbl> 1123.9368, 1320.1675, 1350.1517, 6516.5163, 1457.1448, 7429.9503…
## $ X1939 <dbl> 1138.1382, 1362.4225, 1390.3302, 6628.6924, 1434.2517, 7568.4534…
## $ X1940 <dbl> 1152.5190, 1405.2811, 1431.7044, 6743.1578, 1412.5033, 7592.4912…
## $ X1941 <dbl> 1179.1682, 1472.1728, 1478.5126, 6888.6091, 1420.7092, 7883.1469…
## $ X1942 <dbl> 1206.4336, 1542.0294, 1526.8511, 7037.5077, 1429.8078, 7876.0272…
## $ X1943 <dbl> 1234.3294, 1615.5769, 1576.7700, 7189.8909, 1437.4191, 7717.0854…
## $ X1944 <dbl> 1262.8703, 1692.2995, 1628.3210, 7344.6316, 1445.9415, 8481.8240…
## $ X1945 <dbl> 1292.0711, 1772.2972, 1681.5574, 7502.9222, 1487.2712, 8098.6188…
## $ X1946 <dbl> 1321.9471, 1856.3333, 1736.5342, 7664.8009, 1529.7106, 8706.4492…
## $ X1947 <dbl> 1352.5139, 1944.5435, 1793.3085, 7830.3061, 1573.2881, 9533.8328…
## $ X1948 <dbl> 1383.7875, 2037.0667, 1851.9390, 7999.4766, 1618.0331, 9876.5827…
## $ X1949 <dbl> 1415.7842, 2133.3535, 1912.4864, 8172.3515, 1663.9758, 9526.6768…
## $ X1950 <dbl> 1448.5207, 2234.9246, 1975.0132, 8348.9701, 1711.1469, 9448.5634…
## $ X1951 <dbl> 1499.9625, 2320.5957, 1967.6516, 9064.8592, 1758.1311, 9676.7585…
## $ X1952 <dbl> 1540.1072, 2411.1156, 1974.4452, 9843.4955, 1804.8672, 9020.7066…
## $ X1953 <dbl> 1622.8013, 2503.8650, 2062.0323, 10688.0801, 1854.3109, 9338.148…
## $ X1954 <dbl> 1646.2189, 2436.0178, 2126.2789, 11605.5124, 1905.0473, 9560.983…
## $ X1955 <dbl> 1664.6037, 2631.5320, 2248.2153, 12601.5827, 1957.1090, 10088.51…
## $ X1956 <dbl> 1725.0363, 2582.8010, 2278.5840, 13683.4012, 2008.9198, 10214.85…
## $ X1957 <dbl> 1708.8070, 2828.4404, 2430.4500, 14856.9607, 2063.6990, 10561.48…
## $ X1958 <dbl> 1788.5093, 2977.1485, 2547.0182, 16132.0623, 2363.4767, 11070.58…
## $ X1959 <dbl> 1819.5612, 2984.5717, 2659.3983, 17516.1811, 2706.0981, 10265.97…
## $ X1960 <dbl> 1865.1303, 3098.5848, 2802.6911, 19019.4082, 3097.4406, 11017.09…
## $ X1961 <dbl> 1860.2866, 3505.2652, 2833.7688, 20650.7446, 3533.0366, 11536.45…
## $ X1962 <dbl> 1869.5013, 3403.8314, 2935.6815, 22423.1128, 4029.8618, 11292.77…
## $ X1963 <dbl> 1883.0595, 3572.9312, 3044.2627, 24345.8517, 5349.9733, 10847.22…
## $ X1964 <dbl> 1892.3007, 3969.0230, 3157.1159, 26434.7988, 9298.2882, 11734.27…
## $ X1965 <dbl> 1910.4171, 4259.2211, 3281.6494, 28702.2234, 16155.9310, 12660.5…
## $ X1966 <dbl> 1903.2689, 4497.8862, 3414.2629, 31164.4340, 28071.5711, 12584.1…
## $ X1967 <dbl> 1930.5387, 4751.4962, 3555.0224, 33836.7168, 48773.1144, 12787.1…
## $ X1968 <dbl> 1970.3887, 4670.1082, 3692.8377, 36738.4800, 59932.5844, 13234.6…
## $ X1969 <dbl> 1972.2756, 4795.0600, 3828.9236, 39889.4467, 73641.8131, 14241.8…
## $ X1970 <dbl> 1983.3458, 5090.7505, 3981.9625, 43309.6581, 84894.1671, 14699.6…
## $ X1971 <dbl> 1948.7476, 5178.2863, 4109.9462, 43257.1329, 85963.4879, 15144.4…
## $ X1972 <dbl> 1602.8391, 5160.2660, 4237.3900, 44677.7260, 86134.5584, 15337.8…
## $ X1973 <dbl> 1625.8713, 5464.7349, 4391.2791, 46091.3682, 86371.3252, 15881.6…
## $ X1974 <dbl> 1688.2048, 5399.6289, 4442.8963, 46785.4430, 92799.1153, 16595.3…
## $ X1975 <dbl> 1763.1467, 4155.3419, 4498.0607, 45427.3173, 84992.9455, 16248.0…
## $ X1976 <dbl> 1832.8250, 3831.1875, 4558.4149, 45556.7106, 86190.3712, 16009.8…
## $ X1977 <dbl> 1712.711, 3836.637, 4617.278, 45651.423, 89667.239, 16782.176, 7…
## $ X1978 <dbl> 1811.722, 3772.016, 4690.086, 45191.263, 78291.711, 15943.062, 7…
## $ X1979 <dbl> 1750.081, 3780.290, 4761.107, 44025.270, 88474.524, 16854.544, 6…
## $ X1980 <dbl> 1761.291, 3837.396, 4818.871, 43650.335, 102507.988, 16876.786, …
## $ X1981 <dbl> 1997.826, 3689.767, 4945.846, 42117.002, 102150.342, 15681.658, …
## $ X1982 <dbl> 2240.835, 3602.920, 4995.939, 41080.481, 90360.120, 14981.304, 7…
## $ X1983 <dbl> 2401.310, 3618.094, 4992.611, 40205.213, 83505.668, 15349.644, 7…
## $ X1984 <dbl> 2408.167, 3738.151, 5014.838, 39342.334, 82608.528, 15454.406, 7…
## $ X1985 <dbl> 2373.053, 3599.300, 4925.418, 38727.244, 77696.570, 14211.585, 7…
## $ X1986 <dbl> 2471.176, 3532.103, 5063.914, 38519.766, 60374.516, 15072.944, 7…
## $ X1987 <dbl> 2249.692, 3758.138, 4994.201, 39209.553, 61554.765, 15307.349, 6…
## $ X1988 <dbl> 2066.526, 4071.767, 4904.650, 39780.546, 53561.922, 14860.832, 6…
## $ X1989 <dbl> 1894.496, 4077.400, 5159.301, 40283.550, 59513.725, 13717.075, 7…
## $ X1990 <dbl> 1845.138, 3999.671, 4748.927, 40413.308, 72062.074, 13372.717, 6…
## $ X1991 <dbl> 1705.506, 4046.648, 3460.931, 40034.943, 72004.827, 14492.909, 5…
## $ X1992 <dbl> 1650.799, 3811.346, 3261.805, 39045.704, 73233.770, 15569.354, 3…
## $ X1993 <dbl> 1142.232, 2892.518, 3596.858, 37476.544, 73370.674, 16600.890, 2…
## $ X1994 <dbl> 854.5642, 2922.0132, 3914.7856, 37489.1029, 77636.7456, 17393.65…
## $ X1995 <dbl> 1276.9657, 3341.2075, 4411.3853, 37995.2043, 81887.6857, 16732.7…
## $ X1996 <dbl> 1222.9292, 3765.7755, 4832.7579, 39674.2066, 84634.2940, 17491.6…
## $ X1997 <dbl> 1175.8739, 4003.8450, 4330.4909, 43583.7441, 88626.0696, 18738.3…
## $ X1998 <dbl> 1133.9547, 4149.6461, 4750.6196, 45378.2994, 86027.6322, 19287.3…
## $ X1999 <dbl> 1091.0412, 4193.0997, 5409.6738, 47233.4885, 85689.6813, 18468.7…
## $ X2000 <dbl> 1066.2329, 4267.2055, 5835.3582, 47026.0924, 91954.6163, 18160.2…
## $ X2001 <dbl> 978.7510, 4382.4705, 6389.8144, 47427.6007, 90147.2925, 17209.00…
## $ X2002 <dbl> 1232.3349, 4896.6003, 6714.9047, 47741.8204, 89212.9600, 15203.1…
## $ X2003 <dbl> 1255.6795, 4953.4115, 7125.4537, 51912.9795, 93640.9013, 16409.0…
## $ X2004 <dbl> 1235.3309, 5370.3789, 7560.0073, 54904.4690, 98774.2794, 17741.5…
## $ X2005 <dbl> 1333.4234, 6038.6918, 8026.3556, 60390.5422, 97266.3650, 19145.6…
## $ X2006 <dbl> 1358.1176, 6586.3552, 8559.3966, 65151.9423, 94130.6070, 20506.4…
## $ X2007 <dbl> 1525.2672, 7307.4551, 9143.7998, 66513.2887, 81657.8897, 22162.7…
## $ X2008 <dbl> 1556.2543, 7861.9563, 9907.6161, 63702.5839, 71139.9628, 22864.2…
## $ X2009 <dbl> 1824.023, 7652.625, 10311.404, 64456.972, 59008.924, 21319.016, …
## $ X2010 <dbl> 2027.1567, 7689.8207, 10749.4664, 60467.0860, 56388.8134, 23442.…
## $ X2011 <dbl> 1962.057, 7663.286, 11052.778, 60911.089, 59241.416, 24564.784, …
## $ X2012 <dbl> 2123.8710, 8011.0501, 11227.9504, 57892.7180, 59697.3605, 24037.…
## $ X2013 <dbl> 2166.4020, 8099.6788, 11361.2525, 55672.9160, 62092.8318, 24342.…
## $ X2014 <dbl> 2145.5005, 8183.1646, 11586.8175, 56578.7930, 64063.7840, 23470.…
## $ X2015 <dbl> 2109.7475, 7966.8856, 11878.4376, 56460.5380, 67790.6036, 23853.…
## $ X2016 <dbl> 2102.4519, 7487.9251, 12291.8421, 57455.3080, 70945.2430, 23111.…
## $ X2017 <dbl> 2097.1202, 7216.0614, 12770.9919, 56352.1970, 70883.2893, 23517.…
## $ X2018 <dbl> 2061.7087, 6878.5900, 13317.1193, 56208.1680, 71249.9260, 22670.…
## $ X2019 <dbl> 2080.9411, 6602.2692, 13653.1822, 56330.9480, 71480.5524, 21997.…
## $ X2020 <dbl> 1969.3055, 6029.6919, 13278.3698, 49728.2360, 67383.9701, 19628.…
## $ X2021 <dbl> 1517.0163, 5911.8357, 14595.9444, 52819.9900, 69733.7938, 21527.…
## $ X2022 <dbl> 1386.7554, 5906.1157, 15491.9919, 56415.7830, 74602.8984, 22385.…
## $ X2023 <dbl> 1359.0203, 5778.8343, 16209.8769, 55317.3960, 76528.3216, 21840.…
## $ X2024 <dbl> 1331.8399, 5754.0385, 16760.5702, 55218.6400, 78598.8995, 21026.…
## $ X2025 <dbl> 1331.8399, 5761.2020, 17385.4003, 54957.6230, 81255.0037, 21859.…
## $ X2026 <dbl> 1345.1582, 5783.2739, 18056.1788, 54697.8400, 84098.4282, 22616.…
## $ X2027 <dbl> 1358.6098, 5842.1228, 18721.2726, 54503.9245, 86325.9760, 23208.…
## $ X2028 <dbl> 1385.7820, 5928.3042, 19379.1450, 54375.1055, 87720.1910, 23820.…
## $ X2029 <dbl> 1420.4266, 6049.6914, 20031.6444, 54310.8481, 88310.1349, 24355.…
## $ X2030 <dbl> 1415.5754, 5932.4808, 19367.4691, 51977.6540, 88033.0006, 24043.…
## $ X2031 <dbl> 1441.0557, 6074.2358, 19801.3983, 52578.0857, 88165.5657, 24542.…
## $ X2032 <dbl> 1466.9948, 6219.7766, 20242.4069, 53175.7438, 88295.3808, 25048.…
## $ X2033 <dbl> 1493.4007, 6369.1924, 20690.4638, 53770.4548, 88422.4963, 25559.…
## $ X2034 <dbl> 1520.2819, 6522.5729, 21145.5315, 54362.0499, 88546.9618, 26077.…
## $ X2035 <dbl> 1547.6470, 6680.0094, 21607.5655, 54950.3650, 88668.8264, 26600.…
## $ X2036 <dbl> 1575.5046, 6841.5935, 22076.5145, 55535.2407, 88788.1387, 27130.…
## $ X2037 <dbl> 1603.8637, 7007.4179, 22552.3201, 56116.5227, 88904.9465, 27664.…
## $ X2038 <dbl> 1632.7332, 7177.5757, 23034.9172, 56694.0617, 89019.2969, 28205.…
## $ X2039 <dbl> 1662.1224, 7352.1610, 23524.2332, 57267.7133, 89131.2364, 28751.…
## $ X2040 <dbl> 1692.041, 7531.268, 24020.189, 57837.338, 89240.811, 29302.267, …
## $ X2041 <dbl> 1722.4974, 7714.9920, 24522.6982, 58402.8034, 89348.0659, 29858.…
## $ X2042 <dbl> 1753.5023, 7903.4277, 25031.6672, 58963.9795, 89453.0456, 30419.…
## $ X2043 <dbl> 1785.0654, 8096.6705, 25546.9958, 59520.7435, 89555.7941, 30985.…
## $ X2044 <dbl> 1817.1965, 8294.8160, 26068.5765, 60072.9775, 89656.3546, 31555.…
## $ X2045 <dbl> 1849.906, 8497.960, 26596.295, 60620.569, 89754.770, 32130.040, …
## $ X2046 <dbl> 1883.204, 8706.196, 27130.031, 61163.411, 89851.081, 32708.915, …
## $ X2047 <dbl> 1917.102, 8919.622, 27669.655, 61701.401, 89945.331, 33291.804, …
## $ X2048 <dbl> 1951.610, 9138.330, 28215.035, 62234.443, 90037.559, 33878.516, …
## $ X2049 <dbl> 1986.214, 9362.414, 28766.028, 62762.446, 90127.806, 34468.855, …
## $ X2050 <dbl> 2021.714, 9591.969, 29322.489, 63285.324, 90216.111, 35062.623, …
## $ X2051 <dbl> 2058.137, 9827.087, 29884.262, 63802.996, 90302.512, 35659.616, …
## $ X2052 <dbl> 2095.510, 10067.858, 30451.191, 64315.387, 90387.048, 36259.629,…
## $ X2053 <dbl> 2133.860, 10314.372, 31023.108, 64822.427, 90469.757, 36862.453,…
## $ X2054 <dbl> 2173.215, 10566.720, 31599.844, 65324.050, 90550.674, 37467.876,…
## $ X2055 <dbl> 2213.604, 10824.987, 32181.222, 65820.197, 90629.837, 38075.684,…
## $ X2056 <dbl> 2255.058, 11089.258, 32767.062, 66310.812, 90707.281, 38685.661,…
## $ X2057 <dbl> 2297.608, 11359.619, 33357.176, 66795.846, 90783.041, 39297.591,…
## $ X2058 <dbl> 2341.284, 11636.148, 33951.375, 67275.251, 90857.151, 39911.253,…
## $ X2059 <dbl> 2386.121, 11918.927, 34549.463, 67748.989, 90929.645, 40526.429,…
## $ X2060 <dbl> 2432.151, 12208.031, 35151.240, 68217.022, 91000.556, 41142.897,…
## $ X2061 <dbl> 2479.409, 12503.533, 35756.504, 68679.319, 91069.917, 41760.436,…
## $ X2062 <dbl> 2527.931, 12805.505, 36365.047, 69135.853, 91137.760, 42378.826,…
## $ X2063 <dbl> 2577.753, 13114.015, 36976.660, 69586.601, 91204.116, 42997.844,…
## $ X2064 <dbl> 2628.913, 13429.126, 37591.130, 70031.543, 91269.016, 43617.271,…
## $ X2065 <dbl> 2681.450, 13750.899, 38208.240, 70470.666, 91332.491, 44236.887,…
## $ X2066 <dbl> 2735.402, 14079.392, 38827.774, 70903.959, 91394.570, 44856.473,…
## $ X2067 <dbl> 2790.812, 14414.658, 39449.510, 71331.414, 91455.282, 45475.812,…
## $ X2068 <dbl> 2847.722, 14756.745, 40073.227, 71753.029, 91514.656, 46094.688,…
## $ X2069 <dbl> 2906.173, 15105.698, 40698.702, 72168.805, 91572.721, 46712.888,…
## $ X2070 <dbl> 2966.210, 15461.557, 41325.711, 72578.745, 91629.503, 47330.200,…
## $ X2071 <dbl> 3027.879, 15824.357, 41954.029, 72982.857, 91685.030, 47946.415,…
## $ X2072 <dbl> 3091.227, 16194.128, 42583.430, 73381.151, 91739.328, 48561.328,…
## $ X2073 <dbl> 3156.300, 16570.897, 43213.689, 73773.642, 91792.424, 49174.734,…
## $ X2074 <dbl> 3223.149, 16954.681, 43844.581, 74160.346, 91844.342, 49786.434,…
## $ X2075 <dbl> 3291.823, 17345.497, 44475.881, 74541.284, 91895.109, 50396.232,…
## $ X2076 <dbl> 3362.374, 17743.351, 45107.366, 74916.477, 91944.748, 51003.933,…
## $ X2077 <dbl> 3434.854, 18148.248, 45738.811, 75285.952, 91993.284, 51609.349,…
## $ X2078 <dbl> 3509.319, 18560.184, 46369.997, 75649.736, 92040.740, 52212.294,…
## $ X2079 <dbl> 3585.823, 18979.149, 47000.703, 76007.859, 92087.139, 52812.588,…
## $ X2080 <dbl> 3664.423, 19405.128, 47630.712, 76360.355, 92132.505, 53410.054,…
## $ X2081 <dbl> 3745.178, 19838.100, 48259.808, 76707.257, 92176.858, 54004.519,…
## $ X2082 <dbl> 3828.147, 20278.034, 48887.779, 77048.604, 92220.222, 54595.815,…
## $ X2083 <dbl> 3913.390, 20724.897, 49514.414, 77384.434, 92262.617, 55183.781,…
## $ X2084 <dbl> 4000.970, 21178.647, 50139.506, 77714.788, 92304.065, 55768.257,…
## $ X2085 <dbl> 4090.950, 21639.234, 50762.852, 78039.708, 92344.586, 56349.092,…
## $ X2086 <dbl> 4183.396, 22106.604, 51384.251, 78359.240, 92384.199, 56926.136,…
## $ X2087 <dbl> 4278.374, 22580.694, 52003.508, 78673.430, 92422.926, 57499.246,…
## $ X2088 <dbl> 4375.951, 23061.435, 52620.427, 78982.324, 92460.784, 58068.286,…
## $ X2089 <dbl> 4476.197, 23548.750, 53234.823, 79285.971, 92497.794, 58633.123,…
## $ X2090 <dbl> 4579.183, 24042.557, 53846.509, 79584.423, 92533.972, 59193.629,…
## $ X2091 <dbl> 4684.979, 24542.766, 54455.306, 79877.729, 92569.339, 59749.683,…
## $ X2092 <dbl> 4793.659, 25049.279, 55061.039, 80165.944, 92603.910, 60301.168,…
## $ X2093 <dbl> 4905.298, 25561.993, 55663.536, 80449.120, 92637.705, 60847.973,…
## $ X2094 <dbl> 5019.972, 26080.797, 56262.633, 80727.312, 92670.739, 61389.992,…
## $ X2095 <dbl> 5137.757, 26605.573, 56858.168, 81000.575, 92703.029, 61927.126,…
## $ X2096 <dbl> 5258.733, 27136.198, 57449.985, 81268.966, 92734.592, 62459.279,…
## $ X2097 <dbl> 5382.979, 27672.541, 58037.933, 81532.541, 92765.444, 62986.361,…
## $ X2098 <dbl> 5510.575, 28214.465, 58621.868, 81791.358, 92795.601, 63508.288,…
## $ X2099 <dbl> 5641.604, 28761.827, 59201.649, 82045.475, 92825.077, 64024.981,…
## $ X2100 <dbl> 5776.149, 29314.477, 59777.142, 82294.951, 92853.888, 64536.366,…

Processing Dataframes

Cleaning header names

hdi_clean <- clean_names(hdi)
names(hdi_clean) <- gsub("^x", "", names(hdi_clean))
head(hdi_clean, 1)
##       country  1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  2000
## 1 Afghanistan 0.285 0.291 0.301 0.311 0.305 0.329 0.334 0.338 0.338 0.347 0.351
##    2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013
## 1 0.355 0.383 0.392 0.408 0.417 0.426 0.442 0.446 0.458 0.465 0.474 0.484 0.492
##    2014  2015  2016  2017  2018  2019  2020  2021  2022  2023
## 1 0.497 0.496 0.495 0.496 0.498 0.507 0.501 0.486 0.495 0.496
#changing the columns in broadband to dbl and treat missing values as NA
broadband <- broadband %>%
  mutate(across(c("X2003","X2004","X2005","X2006","X2010","X2011"),
                as.numeric))

broadband_clean <- clean_names(broadband)
names(broadband_clean) <- gsub("^x", "", names(broadband_clean))
head(broadband_clean, 1)
##   country 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
## 1   Aruba   NA   NA   NA   NA   NA 1.52 7.47   13 14.6 16.6 18.8   19 19.2   NA
##   2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
## 1   NA 18.7 18.6 18.2   NA   NA   NA 17.7 17.7 17.7 17.5   NA
gdp_clean <- clean_names(gdp)
names(gdp_clean) <- gsub("^X","", names(gdp))
head(gdp_clean,1)
##   geo        name     1800     1801     1802     1803     1804     1805
## 1 afg Afghanistan 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
##       1806     1807     1808     1809     1810     1811     1812     1813
## 1 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
##       1814     1815     1816     1817     1818     1819     1820     1821
## 1 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
##      1822     1823     1824     1825    1826     1827     1828     1829
## 1 481.844 482.9358 484.0301 485.1268 486.226 487.3277 488.4319 489.5386
##       1830     1831     1832     1833     1834     1835     1836     1837
## 1 490.6478 491.7595 492.8737 493.9905 495.1098 496.2316 497.3559 498.4828
##       1838     1839     1840     1841     1842     1843     1844     1845
## 1 499.6123 500.7443 501.8789 503.0161 504.1558 505.2982 506.4431 507.5906
##       1846     1847     1848     1849     1850     1851     1852     1853
## 1 508.7407 509.8934 511.0487 512.2066 513.3672 514.5304 515.6962 516.8647
##       1854     1855    1856     1857     1858     1859     1860     1861
## 1 518.0358 519.2096 520.386 521.5651 522.7468 523.9313 525.1184 526.3082
##       1862    1863     1864     1865     1866    1867     1868     1869
## 1 527.5008 528.696 529.8939 531.0945 532.2979 533.504 534.7128 535.9243
##       1870     1871     1872     1873     1874     1875     1876     1877
## 1 537.1386 538.3557 539.5755 540.7981 542.0234 543.2515 544.4824 545.7161
##       1878     1879    1880    1881     1882     1883     1884    1885     1886
## 1 546.9526 548.1919 549.434 549.434 556.3763 563.4063 570.5252 577.734 585.0339
##      1887     1888     1889     1890     1891     1892    1893     1894
## 1 592.426 599.9115 607.4916 615.1675 622.9404 630.8115 638.782 646.8533
##       1895    1896     1897    1898     1899     1900     1901    1902     1903
## 1 655.0265 663.303 671.6841 680.171 688.7653 697.4681 706.2808 715.205 724.2418
##       1904     1905     1906     1907     1908     1909     1910    1911
## 1 733.3929 742.6596 752.0434 761.5457 771.1681 780.9121 790.7793 800.771
##       1912    1913     1914     1915    1916     1917     1918    1919     1920
## 1 810.8891 821.135 831.5103 842.0168 852.656 863.4296 874.3393 885.387 896.5742
##       1921     1922    1923     1924     1925     1926    1927     1928
## 1 907.9027 919.3744 930.991 942.7545 954.6665 966.7291 978.944 991.3134
##       1929     1930     1931     1932     1933     1934     1935     1936
## 1 1003.839 1016.523 1029.367 1042.373 1055.544 1068.881 1082.387 1096.064
##       1937     1938     1939     1940     1941     1942     1943    1944
## 1 1109.913 1123.937 1138.138 1152.519 1179.168 1206.434 1234.329 1262.87
##       1945     1946     1947     1948     1949     1950     1951     1952
## 1 1292.071 1321.947 1352.514 1383.787 1415.784 1448.521 1499.963 1540.107
##       1953     1954     1955     1956     1957     1958     1959    1960
## 1 1622.801 1646.219 1664.604 1725.036 1708.807 1788.509 1819.561 1865.13
##       1961     1962    1963     1964     1965     1966     1967     1968
## 1 1860.287 1869.501 1883.06 1892.301 1910.417 1903.269 1930.539 1970.389
##       1969     1970     1971     1972     1973     1974     1975     1976
## 1 1972.276 1983.346 1948.748 1602.839 1625.871 1688.205 1763.147 1832.825
##       1977     1978     1979     1980     1981     1982    1983     1984
## 1 1712.711 1811.722 1750.081 1761.291 1997.826 2240.835 2401.31 2408.167
##       1985     1986     1987     1988     1989     1990     1991     1992
## 1 2373.053 2471.176 2249.692 2066.526 1894.496 1845.138 1705.506 1650.799
##       1993     1994     1995     1996     1997     1998     1999     2000
## 1 1142.232 854.5642 1276.966 1222.929 1175.874 1133.955 1091.041 1066.233
##      2001     2002     2003     2004     2005     2006     2007     2008
## 1 978.751 1232.335 1255.679 1235.331 1333.423 1358.118 1525.267 1556.254
##       2009     2010     2011     2012     2013   2014     2015     2016    2017
## 1 1824.023 2027.157 1962.057 2123.871 2166.402 2145.5 2109.747 2102.452 2097.12
##       2018     2019     2020     2021     2022    2023    2024    2025     2026
## 1 2061.709 2080.941 1969.306 1517.016 1386.755 1359.02 1331.84 1331.84 1345.158
##      2027     2028     2029     2030     2031     2032     2033     2034
## 1 1358.61 1385.782 1420.427 1415.575 1441.056 1466.995 1493.401 1520.282
##       2035     2036     2037     2038     2039     2040     2041     2042
## 1 1547.647 1575.505 1603.864 1632.733 1662.122 1692.041 1722.497 1753.502
##       2043     2044     2045     2046     2047    2048     2049     2050
## 1 1785.065 1817.197 1849.906 1883.204 1917.102 1951.61 1986.214 2021.714
##       2051    2052    2053     2054     2055     2056     2057     2058
## 1 2058.137 2095.51 2133.86 2173.215 2213.604 2255.058 2297.608 2341.284
##       2059     2060     2061     2062     2063     2064    2065     2066
## 1 2386.121 2432.151 2479.409 2527.931 2577.753 2628.913 2681.45 2735.402
##       2067     2068     2069    2070     2071     2072   2073     2074     2075
## 1 2790.812 2847.722 2906.173 2966.21 3027.879 3091.227 3156.3 3223.149 3291.823
##       2076     2077     2078     2079     2080     2081     2082    2083
## 1 3362.374 3434.854 3509.319 3585.823 3664.423 3745.178 3828.147 3913.39
##      2084    2085     2086     2087     2088     2089     2090     2091
## 1 4000.97 4090.95 4183.396 4278.374 4375.951 4476.197 4579.183 4684.979
##       2092     2093     2094     2095     2096     2097     2098     2099
## 1 4793.659 4905.298 5019.972 5137.757 5258.733 5382.979 5510.575 5641.604
##       2100
## 1 5776.149

Checking and removing duplicates

remove_duplicates <- function(data) {
  original_rows <- nrow(data)
  
  data_noDuplicates <- data %>%
    distinct()
  
  new_rows <- nrow(data_noDuplicates)
  
  rows_removed <- original_rows - new_rows
  print_statement <- paste(rows_removed, "rows removed.")
  cat(print_statement)
  
  return(data_noDuplicates)
}

hdi_clean <- remove_duplicates(hdi_clean)
## 0 rows removed.
broadband_clean <- remove_duplicates(broadband_clean)
## 0 rows removed.
gdp_clean <- remove_duplicates(gdp_clean)
## 0 rows removed.

Handling missing values

skim(hdi_clean)
Data summary
Name hdi_clean
Number of rows 193
Number of columns 35
_______________________
Column type frequency:
character 1
numeric 34
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
country 0 1 2 30 0 193 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
1990 52 0.73 0.61 0.17 0.22 0.49 0.64 0.74 0.88 ▂▃▅▇▅
1991 52 0.73 0.61 0.17 0.22 0.49 0.64 0.74 0.88 ▂▃▅▇▅
1992 52 0.73 0.61 0.17 0.22 0.49 0.64 0.74 0.88 ▂▃▅▇▅
1993 51 0.74 0.62 0.17 0.23 0.49 0.64 0.74 0.88 ▂▅▅▇▆
1994 51 0.74 0.62 0.17 0.23 0.49 0.65 0.75 0.89 ▂▅▅▇▆
1995 44 0.77 0.62 0.17 0.24 0.49 0.65 0.75 0.89 ▂▅▅▇▆
1996 44 0.77 0.63 0.17 0.24 0.50 0.66 0.75 0.89 ▂▅▅▇▆
1997 44 0.77 0.64 0.17 0.25 0.50 0.66 0.76 0.90 ▂▅▅▇▆
1998 44 0.77 0.64 0.17 0.26 0.51 0.67 0.77 0.91 ▂▅▅▇▆
1999 40 0.79 0.64 0.17 0.26 0.50 0.67 0.77 0.91 ▂▅▅▇▆
2000 22 0.89 0.65 0.17 0.27 0.50 0.67 0.78 0.92 ▂▅▅▇▅
2001 22 0.89 0.65 0.17 0.27 0.51 0.68 0.79 0.92 ▂▅▅▇▆
2002 21 0.89 0.66 0.17 0.28 0.51 0.68 0.79 0.92 ▂▅▃▇▆
2003 20 0.90 0.66 0.17 0.28 0.53 0.69 0.80 0.93 ▂▅▅▇▆
2004 18 0.91 0.67 0.16 0.29 0.54 0.70 0.81 0.94 ▂▅▅▇▆
2005 7 0.96 0.67 0.16 0.30 0.54 0.70 0.80 0.94 ▂▅▅▇▆
2006 7 0.96 0.68 0.16 0.31 0.55 0.70 0.80 0.94 ▂▅▅▇▆
2007 6 0.97 0.69 0.16 0.31 0.55 0.71 0.81 0.94 ▂▅▅▇▆
2008 5 0.97 0.69 0.16 0.32 0.56 0.72 0.82 0.94 ▂▅▅▇▆
2009 6 0.97 0.70 0.16 0.33 0.56 0.72 0.82 0.94 ▂▅▅▇▆
2010 2 0.99 0.70 0.16 0.34 0.56 0.72 0.82 0.95 ▂▅▅▇▆
2011 1 0.99 0.70 0.16 0.35 0.57 0.72 0.82 0.95 ▂▅▅▇▆
2012 1 0.99 0.71 0.15 0.36 0.58 0.74 0.83 0.95 ▂▅▅▇▇
2013 1 0.99 0.71 0.15 0.36 0.58 0.74 0.83 0.95 ▂▅▅▇▇
2014 1 0.99 0.72 0.15 0.32 0.60 0.74 0.84 0.96 ▁▅▅▇▇
2015 1 0.99 0.72 0.15 0.32 0.60 0.74 0.84 0.96 ▁▅▅▇▇
2016 1 0.99 0.72 0.15 0.30 0.61 0.75 0.84 0.96 ▁▃▅▇▇
2017 1 0.99 0.73 0.15 0.29 0.61 0.75 0.85 0.96 ▁▃▅▇▇
2018 1 0.99 0.73 0.15 0.37 0.61 0.75 0.85 0.97 ▂▅▆▇▇
2019 1 0.99 0.73 0.15 0.28 0.61 0.75 0.85 0.97 ▁▃▅▇▇
2020 1 0.99 0.73 0.15 0.39 0.62 0.74 0.84 0.97 ▂▅▆▇▇
2021 1 0.99 0.73 0.15 0.34 0.61 0.74 0.84 0.97 ▁▃▅▇▆
2022 1 0.99 0.74 0.15 0.38 0.62 0.76 0.85 0.97 ▂▅▅▇▇
2023 0 1.00 0.74 0.15 0.39 0.62 0.76 0.86 0.97 ▂▅▅▇▇ 
skim(broadband_clean)
Data summary
Name broadband_clean
Number of rows 206
Number of columns 27
_______________________
Column type frequency:
character 1
numeric 26
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
country 0 1 2 30 0 206 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
1998 196 0.05 0.18 0.18 0 0.03 0.14 0.26 0.48 ▇▃▃▁▃
1999 192 0.07 0.48 0.59 0 0.02 0.20 0.73 1.91 ▇▃▁▁▁
2000 162 0.21 0.96 1.75 0 0.02 0.19 0.95 8.28 ▇▁▁▁▁
2001 128 0.38 1.41 2.76 0 0.02 0.25 1.26 16.60 ▇▁▁▁▁
2002 100 0.51 1.99 3.67 0 0.02 0.29 2.44 22.00 ▇▁▁▁▁
2003 81 0.61 2.72 4.66 0 0.03 0.40 3.40 23.50 ▇▁▁▁▁
2004 63 0.69 3.74 5.84 0 0.06 0.61 4.89 25.00 ▇▁▁▁▁
2005 39 0.81 4.87 7.62 0 0.07 0.82 6.69 29.50 ▇▁▁▁▁
2006 40 0.81 6.43 9.26 0 0.11 1.67 10.18 37.60 ▇▁▁▁▁
2007 26 0.87 7.28 10.39 0 0.13 1.81 9.56 45.70 ▇▁▁▁▁
2008 18 0.91 8.40 11.28 0 0.14 2.51 12.55 53.60 ▇▁▁▁▁
2009 15 0.93 9.59 12.05 0 0.22 3.72 18.10 62.90 ▇▂▂▁▁
2010 10 0.95 10.09 12.28 0 0.31 4.28 19.27 62.90 ▇▂▂▁▁
2011 17 0.92 10.80 12.10 0 0.51 5.14 20.90 46.90 ▇▂▂▁▁
2012 10 0.95 11.21 12.39 0 0.49 5.12 21.73 46.80 ▇▂▂▂▁
2013 7 0.97 11.87 13.09 0 0.62 6.28 21.25 63.10 ▇▂▂▁▁
2014 7 0.97 12.60 13.43 0 0.92 7.16 22.65 54.70 ▇▂▂▂▁
2015 6 0.97 13.17 13.81 0 0.79 8.07 23.52 49.90 ▇▂▂▂▁
2016 11 0.95 13.65 14.06 0 0.77 8.55 25.35 49.90 ▇▂▂▂▁
2017 10 0.95 14.54 14.78 0 1.15 9.48 27.20 62.20 ▇▂▂▁▁
2018 25 0.88 15.51 14.65 0 1.50 11.50 28.50 54.50 ▇▃▃▂▁
2019 4 0.98 15.43 15.43 0 1.20 10.15 28.37 75.70 ▇▃▂▁▁
2020 6 0.97 16.37 15.40 0 1.66 11.60 29.38 58.10 ▇▂▃▂▁
2021 3 0.99 16.78 15.65 0 1.65 11.60 30.55 59.90 ▇▂▃▂▁
2022 2 0.99 17.40 16.00 0 2.08 12.90 30.15 61.00 ▇▂▃▂▁
2023 49 0.76 19.24 15.90 0 2.97 17.20 31.90 55.90 ▇▃▅▃▁ 
skim(gdp_clean)
Data summary
Name gdp_clean
Number of rows 193
Number of columns 303
_______________________
Column type frequency:
character 2
numeric 301
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
geo 0 1 3 3 0 193 0
name 0 1 2 30 0 193 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
1800 0 1 1031.02 577.84 354.84 636.43 918.88 1220.37 4789.23 ▇▂▁▁▁
1801 0 1 1030.16 574.69 354.84 638.22 919.16 1220.37 4705.67 ▇▂▁▁▁
1802 0 1 1032.78 585.38 354.84 638.87 920.99 1215.63 4965.51 ▇▂▁▁▁
1803 0 1 1032.26 579.14 354.84 639.52 924.19 1209.39 4857.91 ▇▂▁▁▁
1804 0 1 1035.23 589.37 354.84 640.17 925.92 1203.18 5089.13 ▇▂▁▁▁
1805 0 1 1035.10 582.67 354.84 640.82 924.17 1197.00 4789.23 ▇▂▁▁▁
1806 0 1 1036.27 583.78 354.84 642.62 925.22 1191.59 4825.86 ▇▂▁▁▁
1807 0 1 1036.19 576.65 354.84 643.27 925.92 1201.89 4421.80 ▇▂▁▁▁
1808 0 1 1027.36 533.24 354.84 643.93 927.01 1187.63 3720.13 ▇▃▁▁▁
1809 0 1 1027.92 537.90 354.84 644.58 927.01 1188.66 3787.66 ▇▃▁▁▁
1810 0 1 1029.69 545.78 354.84 645.23 927.01 1189.70 3981.11 ▇▃▁▁▁
1811 0 1 1026.78 542.91 354.84 647.05 927.01 1191.76 3888.39 ▇▃▁▁▁
1812 0 1 1025.44 535.81 354.84 647.27 921.63 1192.79 3670.91 ▇▃▁▁▁
1813 0 1 1031.54 546.30 354.84 647.27 924.88 1194.86 3809.41 ▇▃▁▁▁
1814 0 1 1034.93 549.76 354.84 647.27 927.01 1195.89 3667.47 ▇▃▁▁▁
1815 0 1 1036.35 558.36 354.84 647.65 923.13 1196.92 3993.70 ▇▃▁▁▁
1816 0 1 1033.94 543.37 354.84 651.50 923.13 1198.98 3720.13 ▇▃▁▁▁
1817 0 1 1033.52 542.78 354.84 652.17 923.13 1200.02 3723.56 ▇▃▁▁▁
1818 0 1 1038.22 548.05 354.84 652.45 929.73 1201.05 3664.04 ▇▃▁▁▁
1819 0 1 1038.56 545.00 354.84 652.45 925.62 1204.16 3551.86 ▇▃▁▁▁
1820 0 1 1044.51 559.75 354.84 652.45 936.33 1211.32 3784.23 ▇▃▁▁▁
1821 0 1 1050.96 565.81 354.84 652.45 931.55 1218.51 3786.52 ▇▃▁▁▁
1822 0 1 1055.37 569.17 354.84 652.45 938.64 1239.66 3794.53 ▇▃▁▁▁
1823 0 1 1061.23 575.77 354.84 654.66 944.26 1244.24 3844.89 ▇▃▁▁▁
1824 0 1 1068.13 586.03 354.84 656.88 945.31 1240.36 3997.13 ▇▃▁▁▁
1825 0 1 1072.69 586.86 355.66 659.09 953.25 1247.73 4030.33 ▇▃▁▁▁
1826 0 1 1078.36 586.57 356.99 660.57 957.35 1255.15 3765.91 ▇▃▁▁▁
1827 0 1 1085.43 598.88 358.97 667.62 955.80 1262.55 4005.14 ▇▃▁▁▁
1828 0 1 1090.69 602.41 360.30 677.62 958.98 1268.28 3961.65 ▇▃▁▁▁
1829 0 1 1096.83 605.06 361.62 688.73 963.13 1276.37 3941.04 ▇▃▁▁▁
1830 0 1 1103.09 609.60 362.95 694.81 966.09 1285.24 4063.52 ▇▃▁▁▁
1831 0 1 1108.51 620.20 364.93 697.09 966.09 1292.88 4057.80 ▇▃▁▁▁
1832 0 1 1117.13 636.11 366.26 700.53 966.09 1301.13 4134.49 ▇▃▁▁▁
1833 0 1 1122.99 640.14 367.58 702.82 967.04 1326.88 4119.61 ▇▃▁▁▁
1834 0 1 1129.59 643.30 369.57 705.11 970.07 1333.74 4165.40 ▇▃▁▁▁
1835 0 1 1140.76 661.71 370.89 708.03 970.09 1340.64 4419.51 ▇▂▁▁▁
1836 0 1 1144.69 666.73 372.22 709.69 970.44 1347.57 4506.50 ▇▂▁▁▁
1837 0 1 1150.65 667.55 374.21 711.97 979.21 1354.54 4414.93 ▇▂▁▁▁
1838 0 1 1155.41 673.01 375.53 714.26 982.28 1360.09 4536.26 ▇▂▁▁▁
1839 0 1 1157.67 672.55 376.85 716.55 985.35 1362.01 4442.40 ▇▂▁▁▁
1840 0 1 1166.84 682.31 378.84 718.84 992.42 1370.27 4599.22 ▇▂▁▁▁
1841 0 1 1170.18 678.84 380.17 722.28 995.85 1379.55 4457.28 ▇▃▁▁▁
1842 0 1 1172.71 674.57 381.49 722.93 1000.43 1387.81 4358.84 ▇▃▁▁▁
1843 0 1 1177.78 681.43 383.48 722.93 995.85 1397.09 4521.38 ▇▃▁▁▁
1844 0 1 1188.78 704.36 384.80 724.30 1000.88 1404.36 4859.06 ▇▂▁▁▁
1845 0 1 1194.94 712.34 385.58 724.30 1011.51 1411.62 5021.60 ▇▂▁▁▁
1846 0 1 1199.88 716.42 386.60 724.30 1007.16 1418.92 4960.93 ▇▂▁▁▁
1847 0 1 1207.42 727.63 386.60 724.57 1017.88 1426.26 4846.47 ▇▂▁▁▁
1848 0 1 1217.54 745.97 387.62 728.00 1021.26 1433.64 4991.84 ▇▂▁▁▁
1849 0 1 1225.08 754.39 387.62 731.43 1028.19 1441.05 5061.66 ▇▂▁▁▁
1850 0 1 1234.79 759.63 388.64 733.72 1029.04 1462.10 4958.64 ▇▂▁▁▁
1851 0 1 1250.78 784.05 388.64 733.72 1050.41 1471.39 5124.62 ▇▂▁▁▁
1852 0 1 1266.50 818.84 389.67 733.72 1055.96 1481.18 5295.17 ▇▂▁▁▁
1853 0 1 1279.42 842.86 389.67 733.72 1062.66 1531.55 5492.05 ▇▂▁▁▁
1854 0 1 1287.23 845.85 390.69 733.72 1064.50 1537.27 5619.11 ▇▂▁▁▁
1855 0 1 1295.99 842.09 390.69 733.72 1066.34 1542.99 5434.82 ▇▂▁▁▁
1856 0 1 1305.60 880.62 391.71 733.72 1068.18 1537.35 5738.15 ▇▂▁▁▁
1857 0 1 1318.67 879.75 391.71 737.16 1070.02 1554.44 5745.02 ▇▂▁▁▁
1858 0 1 1321.07 867.37 392.73 737.16 1071.87 1560.16 5584.77 ▇▂▁▁▁
1859 0 1 1335.60 902.05 393.76 737.16 1073.72 1565.89 5758.76 ▇▂▁▁▁
1860 0 1 1348.87 920.74 393.76 738.89 1075.57 1571.61 5821.71 ▇▂▁▁▁
1861 0 1 1352.61 916.99 394.78 741.21 1077.43 1577.33 5750.74 ▇▂▁▁▁
1862 0 1 1356.53 921.33 394.78 745.13 1080.38 1584.20 5453.13 ▇▂▁▁▁
1863 0 1 1371.62 951.59 395.80 747.75 1094.29 1589.92 5912.14 ▇▂▁▁▁
1864 0 1 1384.09 972.97 395.80 751.67 1102.51 1600.36 6015.16 ▇▂▁▁▁
1865 0 1 1387.93 971.99 396.83 755.59 1111.08 1610.68 6104.44 ▇▂▁▁▁
1866 0 1 1397.25 986.20 396.83 759.51 1118.76 1621.00 6239.51 ▇▂▁▁▁
1867 0 1 1406.59 998.28 397.85 763.43 1116.82 1616.25 6208.60 ▇▂▁▁▁
1868 0 1 1420.72 1024.89 397.85 766.05 1114.85 1642.67 6451.27 ▇▂▁▁▁
1869 0 1 1435.94 1043.49 398.87 769.97 1112.12 1683.79 6450.13 ▇▂▁▁▁
1870 0 1 1449.71 1064.61 398.87 772.93 1110.13 1733.01 6672.19 ▇▂▁▁▁
1871 0 1 1460.78 1074.88 399.89 774.80 1123.38 1702.10 6635.56 ▇▂▁▁▁
1872 0 1 1488.56 1127.48 399.89 777.66 1137.78 1767.43 6603.51 ▇▂▁▁▁
1873 0 1 1509.13 1166.71 400.92 781.22 1148.75 1802.83 6990.40 ▇▂▁▁▁
1874 0 1 1516.84 1166.80 400.92 783.86 1156.10 1787.95 7012.15 ▇▂▁▁▁
1875 0 1 1525.90 1178.69 401.94 785.62 1160.68 1820.36 7550.14 ▇▂▁▁▁
1876 0 1 1534.54 1174.66 401.94 787.38 1155.16 1838.35 7310.91 ▇▂▁▁▁
1877 0 1 1545.32 1194.07 402.96 789.14 1157.89 1856.52 7363.56 ▇▂▁▁▁
1878 0 1 1557.62 1223.79 402.96 790.90 1172.13 1851.34 7803.11 ▇▂▁▁▁
1879 0 1 1558.78 1192.05 403.98 793.55 1176.70 1874.89 7672.62 ▇▂▁▁▁
1880 0 1 1587.75 1247.70 403.98 795.31 1180.14 1898.43 7817.99 ▇▂▁▁▁
1881 0 1 1599.41 1269.20 404.19 797.07 1184.72 1921.97 8128.19 ▇▂▁▁▁
1882 0 1 1617.21 1274.21 407.46 799.01 1206.47 1941.33 7506.64 ▇▂▁▁▁
1883 0 1 1636.07 1312.21 410.74 804.71 1193.82 1964.14 8164.82 ▇▂▁▁▁
1884 0 1 1649.45 1320.25 414.01 810.45 1201.84 1981.96 7853.47 ▇▂▁▁▁
1885 0 1 1660.16 1330.85 417.28 820.72 1209.92 2008.50 8068.67 ▇▂▁▁▁
1886 0 1 1672.23 1341.53 420.55 822.04 1225.43 2028.36 7898.11 ▇▂▁▁▁
1887 0 1 1702.50 1368.49 424.15 834.06 1247.67 2060.56 8450.98 ▇▂▁▁▁
1888 0 1 1718.11 1383.64 427.43 839.37 1237.37 2071.82 8217.47 ▇▂▁▁▁
1889 0 1 1725.67 1418.23 431.03 827.58 1242.76 2086.13 8661.60 ▇▂▁▁▁
1890 0 1 1735.22 1407.29 434.30 840.35 1241.57 2109.75 8133.91 ▇▂▁▁▁
1891 0 1 1733.27 1414.84 437.90 831.02 1245.29 2045.50 8513.94 ▇▂▁▁▁
1892 0 1 1754.77 1417.07 441.50 834.06 1249.02 2129.06 8383.45 ▇▂▁▁▁
1893 0 1 1774.36 1401.71 445.10 853.44 1276.50 2172.92 7822.57 ▇▂▁▁▁
1894 0 1 1808.43 1424.64 448.70 864.21 1339.21 2230.93 7842.03 ▇▂▁▁▁
1895 0 1 1822.92 1458.62 452.30 842.87 1293.71 2237.80 8195.72 ▇▂▁▁▁
1896 0 1 1851.96 1500.33 455.90 843.28 1362.59 2245.81 8254.10 ▇▂▁▁▁
1897 0 1 1857.44 1496.98 459.50 855.06 1339.21 2260.11 8477.31 ▇▂▁▁▁
1898 0 1 1890.47 1540.10 463.43 851.62 1373.81 2322.58 8584.91 ▇▂▁▁▁
1899 0 1 1911.94 1583.57 467.03 847.69 1405.22 2305.01 9110.30 ▇▂▁▁▁
1900 0 1 1919.82 1595.02 467.63 853.68 1407.12 2330.88 9200.73 ▇▂▁▁▁
1901 0 1 1940.09 1612.20 468.22 858.92 1438.20 2323.65 10038.62 ▇▂▁▁▁
1902 0 1 1972.19 1640.42 468.82 864.91 1466.07 2357.99 9940.18 ▇▂▁▁▁
1903 0 1 1989.23 1672.38 469.41 870.90 1467.45 2397.50 10234.35 ▇▂▁▁▁
1904 0 1 2015.84 1690.26 470.00 878.63 1481.34 2426.67 9917.28 ▇▂▁▁▁
1905 0 1 2028.11 1739.07 470.60 896.26 1449.20 2462.15 10440.39 ▇▂▁▁▁
1906 0 1 2064.44 1823.08 471.19 899.70 1415.94 2487.33 11423.65 ▇▂▁▁▁
1907 0 1 2078.36 1848.10 471.79 894.86 1443.41 2493.71 11390.45 ▇▂▁▁▁
1908 0 1 2088.06 1800.08 472.39 900.85 1486.91 2572.04 10274.42 ▇▂▁▁▁
1909 0 1 2126.31 1859.93 472.98 906.84 1516.34 2608.67 11215.32 ▇▂▁▁▁
1910 0 1 2168.72 1913.10 473.58 912.29 1578.48 2568.38 11031.03 ▇▂▁▁▁
1911 0 1 2182.55 1944.77 474.18 914.58 1534.77 2593.77 11144.35 ▇▂▁▁▁
1912 0 1 2217.93 1982.40 474.78 918.01 1587.97 2595.14 11420.21 ▇▂▁▁▁
1913 0 1 2240.61 1998.74 475.38 921.45 1625.41 2607.52 11570.16 ▇▂▁▁▁
1914 0 1 2198.21 1880.37 475.98 932.89 1590.96 2646.44 10411.77 ▇▂▁▁▁
1915 0 1 2200.53 1873.98 476.59 943.20 1630.98 2561.74 10489.61 ▇▂▁▁▁
1916 0 1 2219.99 1951.31 477.19 954.64 1621.78 2586.53 11700.66 ▇▂▁▁▁
1917 0 1 2186.84 1888.13 477.79 924.73 1585.39 2602.66 11182.13 ▇▂▁▁▁
1918 0 1 2132.14 1858.98 478.40 927.64 1543.27 2577.76 11985.67 ▇▁▁▁▁
1919 0 1 2155.18 1884.34 479.00 891.69 1560.16 2601.49 11961.64 ▇▁▁▁▁
1920 0 1 2190.00 1925.58 480.44 897.41 1614.47 2633.85 11621.67 ▇▂▁▁▁
1921 0 1 2199.18 1869.64 480.44 907.90 1637.92 2712.83 11074.53 ▇▂▁▁▁
1922 0 1 2290.23 1976.44 479.82 935.18 1640.29 2761.57 11457.99 ▇▂▁▁▁
1923 0 1 2349.85 2068.71 480.13 973.82 1663.18 2801.69 12672.47 ▇▂▁▁▁
1924 0 1 2425.85 2135.42 480.48 1010.43 1700.96 2851.33 12736.57 ▇▂▁▁▁
1925 0 1 2495.65 2183.07 481.18 1034.31 1713.55 2890.25 12762.89 ▇▂▁▁▁
1926 0 1 2544.58 2216.93 481.87 1055.99 1775.20 2939.31 13332.93 ▇▂▁▁▁
1927 0 1 2594.21 2275.56 482.55 1077.67 1824.58 2964.65 13200.15 ▇▂▁▁▁
1928 0 1 2679.13 2361.32 483.16 1096.58 1878.38 3031.51 13107.44 ▇▂▁▁▁
1929 0 1 2739.06 2414.21 483.83 1074.58 1957.36 3138.64 13683.20 ▇▂▁▁▁
1930 0 1 2716.03 2344.06 484.48 1080.67 1984.33 3128.15 12242.08 ▇▂▁▁▁
1931 0 1 2646.27 2218.31 485.13 1099.60 1988.82 3131.56 11509.50 ▇▂▁▁▁
1932 0 1 2591.01 2138.16 485.77 1132.32 1932.31 3104.77 11162.67 ▇▂▁▁▁
1933 0 1 2653.73 2183.49 486.41 1129.42 1956.21 3180.71 11456.84 ▇▂▁▁▁
1934 0 1 2733.24 2265.20 487.03 1158.39 1979.39 3255.40 11443.11 ▇▂▁▁▁
1935 0 1 2820.12 2345.05 487.65 1172.55 2109.60 3421.73 11081.40 ▇▂▁▁▁
1936 0 1 2911.97 2454.22 488.25 1219.06 2153.09 3479.22 12096.71 ▇▃▁▁▁
1937 0 1 3021.21 2582.38 488.85 1183.57 2206.89 3537.67 12928.87 ▇▂▁▁▁
1938 0 1 3061.19 2623.31 489.43 1188.14 2232.08 3569.03 12048.63 ▇▃▁▁▁
1939 0 1 3144.17 2741.05 490.01 1219.61 2251.53 3596.93 12786.93 ▇▂▁▁▁
1940 0 1 3139.40 2755.33 490.57 1230.50 2331.66 3653.58 13866.34 ▇▂▁▁▁
1941 0 1 3190.26 2890.90 488.47 1250.15 2319.17 3662.23 16170.12 ▇▂▁▁▁
1942 0 1 3230.57 3064.59 486.37 1240.13 2328.94 3691.41 18857.01 ▇▂▁▁▁
1943 0 1 3271.56 3235.95 484.26 1266.79 2307.09 3696.57 21989.43 ▇▁▁▁▁
1944 0 1 3298.81 3409.61 482.16 1306.10 2258.95 3679.89 25641.64 ▇▁▁▁▁
1945 0 1 3280.53 3510.75 480.05 1282.92 2243.71 3637.04 29898.19 ▇▁▁▁▁
1946 0 1 3395.65 3768.57 477.94 1203.11 2240.71 3727.89 34860.21 ▇▁▁▁▁
1947 0 1 3537.65 4106.10 476.51 1226.89 2368.37 3969.36 40645.98 ▇▁▁▁▁
1948 0 1 3725.53 4485.56 475.77 1315.50 2542.00 4116.00 47389.30 ▇▁▁▁▁
1949 0 1 3888.06 4956.80 475.71 1324.76 2655.06 4335.98 55247.60 ▇▁▁▁▁
1950 0 1 4068.84 5569.89 476.00 1334.08 2574.85 4473.90 64408.73 ▇▁▁▁▁
1951 0 1 4193.45 5676.11 477.62 1472.36 2681.02 4611.08 65466.77 ▇▁▁▁▁
1952 0 1 4309.94 5804.11 479.01 1444.80 2755.08 4633.55 66539.37 ▇▁▁▁▁
1953 0 1 4471.93 5967.12 480.68 1499.46 2801.68 4815.28 67628.24 ▇▁▁▁▁
1954 0 1 4643.08 6135.83 483.20 1525.27 2929.55 5345.79 68731.90 ▇▁▁▁▁
1955 0 1 4842.55 6340.54 486.86 1538.96 3093.28 5456.09 69851.00 ▇▁▁▁▁
1956 0 1 5033.51 6531.21 490.20 1599.21 3136.35 5472.07 70987.18 ▇▁▁▁▁
1957 0 1 5210.38 6728.59 493.35 1655.23 3344.68 5860.88 72138.97 ▇▁▁▁▁
1958 0 1 5357.76 6918.39 497.44 1639.12 3398.18 5899.94 73306.97 ▇▁▁▁▁
1959 0 1 5553.99 7202.17 499.83 1667.91 3440.83 6301.18 74490.74 ▇▁▁▁▁
1960 0 1 5840.49 7552.96 501.78 1712.91 3605.66 6470.70 75691.87 ▇▁▁▁▁
1961 0 1 6117.62 7970.15 503.02 1745.31 3715.60 7237.65 76894.78 ▇▁▁▁▁
1962 0 1 6396.45 8477.40 515.07 1788.41 3785.59 7549.14 78114.33 ▇▁▁▁▁
1963 0 1 6693.92 9128.60 519.27 1774.25 3844.69 7884.42 79351.04 ▇▁▁▁▁
1964 0 1 7166.34 10029.67 530.35 1816.37 4033.75 8393.89 80603.46 ▇▁▁▁▁
1965 0 1 7475.32 10373.07 540.95 1852.56 4198.33 8585.44 81875.08 ▇▁▁▁▁
1966 0 1 7898.15 11311.56 551.82 1910.64 4403.45 9188.80 85437.76 ▇▁▁▁▁
1967 0 1 8269.11 12075.41 583.91 1913.99 4457.18 9471.34 95119.00 ▇▁▁▁▁
1968 0 1 8683.79 12784.17 603.65 1926.72 4596.16 10450.80 100793.96 ▇▁▁▁▁
1969 0 1 9090.14 13308.88 630.14 2044.24 4612.47 10635.22 99178.96 ▇▁▁▁▁
1970 0 1 9600.98 14362.53 622.55 2008.56 4773.29 10987.22 92944.06 ▇▁▁▁▁
1971 0 1 9936.71 14951.05 635.77 2063.64 4981.62 11481.84 102400.18 ▇▁▁▁▁
1972 0 1 10291.80 15390.89 638.35 2187.80 5273.91 12414.28 107043.88 ▇▁▁▁▁
1973 0 1 10719.57 15839.91 636.58 2291.34 5448.15 13137.90 114683.01 ▇▁▁▁▁
1974 0 1 11005.68 15920.69 635.59 2498.57 5827.08 13592.08 106784.26 ▇▁▁▁▁
1975 0 1 10870.43 15277.52 620.28 2502.81 5885.61 13216.74 103552.16 ▇▁▁▁▁
1976 0 1 11213.74 15928.71 613.76 2578.10 5999.57 13890.74 117272.23 ▇▁▁▁▁
1977 0 1 11402.43 15919.20 589.08 2544.72 6079.33 14139.69 124432.05 ▇▁▁▁▁
1978 0 1 11601.66 15989.35 566.00 2613.44 6103.22 14232.69 127347.21 ▇▁▁▁▁
1979 0 1 12050.49 17330.92 551.79 2605.31 6646.94 14397.51 149806.43 ▇▁▁▁▁
1980 0 1 12002.78 16860.78 547.28 2550.49 6086.50 15259.07 133984.05 ▇▁▁▁▁
1981 0 1 11747.60 15414.14 559.98 2603.92 6376.58 15120.16 103485.07 ▇▁▁▁▁
1982 0 1 11570.06 14736.51 518.36 2622.00 6319.58 15187.47 103779.56 ▇▁▁▁▁
1983 0 1 11493.47 14326.03 444.13 2567.55 6109.49 15264.61 100724.40 ▇▂▁▁▁
1984 0 1 11708.66 14435.25 414.08 2462.10 6560.62 15473.56 97898.49 ▇▂▁▁▁
1985 0 1 11738.30 14186.36 396.66 2607.10 6408.94 15442.21 93169.30 ▇▂▁▁▁
1986 0 1 11814.08 13815.28 389.61 2772.19 6600.42 15840.02 89190.03 ▇▂▁▁▁
1987 0 1 11982.56 14040.89 433.69 2757.78 6702.59 15307.35 92345.64 ▇▂▁▁▁
1988 0 1 12204.70 14244.79 469.74 2807.87 6813.60 15528.20 97581.75 ▇▂▁▁▁
1989 0 1 12466.78 14757.63 494.59 2806.69 6915.93 15591.19 102690.61 ▇▂▁▁▁
1990 0 1 12538.44 15106.53 491.67 2814.66 6703.43 16008.23 106474.19 ▇▂▁▁▁
1991 0 1 12435.10 15269.92 499.77 2714.96 7032.45 15214.72 108602.20 ▇▂▁▁▁
1992 0 1 12440.87 15582.14 449.51 2689.95 6903.56 14748.06 111141.86 ▇▂▁▁▁
1993 0 1 12501.16 15803.07 475.69 2654.67 6522.42 14910.93 111208.47 ▇▂▁▁▁
1994 0 1 12793.78 16392.11 476.12 2609.85 6619.48 14997.29 114837.99 ▇▂▁▁▁
1995 0 1 13168.74 16933.20 460.36 2747.53 6562.11 14689.07 118539.89 ▇▂▁▁▁
1996 0 1 13557.72 17335.59 500.17 2825.32 6758.75 15510.48 121244.46 ▇▂▁▁▁
1997 0 1 14094.75 18030.63 542.78 3005.81 7079.87 16470.05 125465.22 ▇▂▁▁▁
1998 0 1 14459.48 18562.14 585.28 3009.40 7301.64 17121.58 131460.65 ▇▂▁▁▁
1999 0 1 14800.51 19192.96 634.38 3029.82 7481.41 16962.52 137399.19 ▇▂▁▁▁
2000 0 1 15375.68 20049.69 623.86 3063.37 7822.52 18099.07 144346.88 ▇▂▁▁▁
2001 0 1 15626.64 20329.44 656.81 2995.55 8015.49 17923.57 148492.49 ▇▂▁▁▁
2002 0 1 15887.49 20577.19 661.56 3110.64 8022.86 18330.26 151397.53 ▇▂▁▁▁
2003 0 1 16319.53 21039.45 683.69 3162.97 8287.74 19845.48 154387.03 ▇▂▁▁▁
2004 0 1 17033.77 21914.58 713.52 3213.12 8700.97 20777.53 159591.41 ▇▂▁▁▁
2005 0 1 17569.16 22331.03 738.94 3306.87 8922.88 21808.48 163814.55 ▇▂▁▁▁
2006 0 1 18275.94 23089.88 758.23 3445.07 9566.50 23464.50 172260.42 ▇▂▁▁▁
2007 0 1 18966.69 24128.88 784.18 3543.62 9816.02 25594.17 195076.50 ▇▁▁▁▁
2008 0 1 19100.62 23773.91 810.21 3518.02 10314.15 25769.52 194605.84 ▇▁▁▁▁
2009 0 1 18297.01 22224.04 801.94 3543.01 10258.97 24057.73 177404.79 ▇▂▁▁▁
2010 0 1 18639.26 22624.93 804.35 3735.79 10636.24 24452.28 181527.16 ▇▁▁▁▁
2011 0 1 19006.41 23334.12 807.66 3632.28 10933.21 25020.78 194719.44 ▇▁▁▁▁
2012 0 1 19188.38 23231.10 506.72 3756.93 11098.89 26141.69 194719.44 ▇▁▁▁▁
2013 0 1 19297.48 23176.97 624.18 3950.88 11361.25 26275.42 194065.70 ▇▁▁▁▁
2014 0 1 19529.30 23279.43 614.46 4012.55 11767.16 26497.57 195343.02 ▇▁▁▁▁
2015 0 1 19814.46 23548.18 594.92 4163.92 12030.38 27545.36 197056.17 ▇▁▁▁▁
2016 0 1 20071.81 23788.31 501.11 4297.84 12336.90 27993.19 199583.99 ▇▁▁▁▁
2017 0 1 20411.26 23994.38 458.77 4458.47 12497.82 28895.47 201458.07 ▇▂▁▁▁
2018 0 1 20774.51 24299.11 435.41 4464.81 13218.92 30185.56 203540.00 ▇▂▁▁▁
2019 0 1 21051.17 24559.09 425.94 4775.38 13215.57 30674.02 205749.33 ▇▁▁▁▁
2020 0 1 19942.80 24111.13 386.68 4615.54 12407.79 26953.15 207844.68 ▇▁▁▁▁
2021 0 1 21053.87 25312.17 395.80 4745.64 13045.93 30416.79 210111.00 ▇▁▁▁▁
2022 0 1 21823.61 25998.30 364.70 4591.33 13148.07 33117.84 213893.00 ▇▂▁▁▁
2023 0 1 22007.15 25865.53 354.18 4733.08 13416.93 33781.34 217743.07 ▇▂▁▁▁
2024 0 1 22369.00 26193.73 363.48 4964.38 13397.20 34182.07 221662.45 ▇▂▁▁▁
2025 0 1 22884.93 26732.70 377.24 5050.45 13623.32 34548.44 225652.37 ▇▂▁▁▁
2026 0 1 23408.74 27396.00 385.82 5076.46 13999.36 35411.54 229714.11 ▇▁▁▁▁
2027 0 1 23899.94 27910.17 392.67 5191.12 14362.97 36425.99 233848.97 ▇▁▁▁▁
2028 0 1 24436.57 28430.09 398.18 5351.39 14954.81 37528.62 238058.25 ▇▁▁▁▁
2029 0 1 25012.21 28973.00 400.98 5549.51 15659.08 38730.86 242343.30 ▇▁▁▁▁
2030 0 1 24606.39 28759.43 391.26 5454.60 15394.08 38220.76 244056.21 ▇▁▁▁▁
2031 0 1 24878.01 28538.57 398.30 5583.59 15752.15 38828.88 237220.87 ▇▁▁▁▁
2032 0 1 25154.90 28342.85 405.47 5716.06 16116.88 39438.83 230818.57 ▇▂▁▁▁
2033 0 1 25436.83 28169.74 412.77 5852.09 16488.29 40050.38 224812.89 ▇▂▁▁▁
2034 0 1 25723.60 28016.99 420.20 5991.77 16866.38 40663.32 219171.21 ▇▂▁▁▁
2035 0 1 26015.02 27882.62 427.76 6135.18 17251.16 41277.42 213864.26 ▇▂▁▁▁
2036 0 1 26310.92 27764.82 435.46 6282.42 17642.62 41892.48 208865.67 ▇▂▁▁▁
2037 0 1 26611.13 27662.00 443.30 6433.57 18040.75 42508.26 204151.69 ▇▂▁▁▁
2038 0 1 26915.52 27572.71 451.28 6588.72 18445.55 43124.54 199700.83 ▇▂▁▁▁
2039 0 1 27223.93 27495.66 459.40 6747.96 18856.98 43741.12 195493.63 ▇▂▁▁▁
2040 0 1 27536.26 27429.67 467.67 6911.39 19275.02 44357.76 191512.42 ▇▂▁▁▁
2041 0 1 27852.37 27373.70 476.09 7079.10 19699.63 44974.26 187741.14 ▇▂▁▁▁
2042 0 1 28172.16 27326.78 484.66 7251.18 20130.78 45590.40 184165.16 ▇▃▁▁▁
2043 0 1 28495.50 27288.04 493.38 7427.72 20568.43 46205.96 180771.10 ▇▃▁▁▁
2044 0 1 28822.31 27256.70 502.26 7608.82 21012.50 46820.73 177546.77 ▇▃▁▁▁
2045 0 1 29152.47 27232.04 511.30 7794.58 21462.96 47434.51 174480.96 ▇▃▁▁▁
2046 0 1 29485.91 27213.40 520.51 7985.09 21919.72 48047.08 171563.42 ▇▃▁▁▁
2047 0 1 29822.53 27200.16 529.88 8180.43 22382.73 48658.25 168784.71 ▇▃▁▁▁
2048 0 1 30162.23 27191.80 539.41 8380.72 22851.89 49267.81 166136.15 ▇▃▁▁▁
2049 0 1 30504.95 27187.80 549.12 8586.05 23327.13 49875.56 163609.74 ▇▃▁▁▁
2050 0 1 30850.58 27187.70 559.01 8796.50 23808.34 50481.32 161198.11 ▇▃▁▁▁
2051 0 1 31199.07 27191.06 569.07 9012.17 24295.44 51084.89 158894.42 ▇▃▁▁▁
2052 0 1 31550.34 27197.48 579.31 9233.16 24788.32 51686.08 156692.38 ▇▃▂▁▁
2053 0 1 31904.30 27206.61 589.74 9459.56 25286.86 52284.72 154586.12 ▇▃▂▁▁
2054 0 1 32260.89 27218.11 600.36 9691.46 25790.94 52880.63 152570.24 ▇▃▂▁▁
2055 0 1 32620.03 27231.65 611.16 9928.96 26300.45 53473.62 150639.67 ▇▃▂▁▁
2056 0 1 32981.66 27246.96 622.16 10172.14 26815.25 54063.54 148789.75 ▇▃▂▁▁
2057 0 1 33345.70 27263.76 633.36 10421.09 27335.21 54650.22 147016.10 ▇▃▂▁▁
2058 0 1 33712.08 27281.80 644.76 10675.90 27860.18 55233.49 145314.66 ▇▅▂▁▁
2059 0 1 34080.75 27300.85 656.37 10936.66 28390.01 55813.21 143681.63 ▇▃▃▁▁
2060 0 1 34451.62 27320.68 668.18 11203.44 28924.56 56389.23 142113.48 ▇▅▃▁▁
2061 0 1 34824.64 27341.09 680.21 11476.34 29463.66 56961.39 140606.89 ▇▅▃▁▁
2062 0 1 35199.75 27361.89 692.45 11755.43 30007.15 57553.49 139158.76 ▇▅▃▁▁
2063 0 1 35576.87 27382.91 704.92 12040.78 30554.86 58207.61 137766.19 ▇▅▃▁▁
2064 0 1 35955.94 27403.97 717.61 12332.48 31106.62 58858.50 136426.45 ▇▅▃▁▁
2065 0 1 36336.89 27424.93 730.52 12630.59 31662.25 59505.98 135136.99 ▇▅▃▁▁
2066 0 1 36719.67 27445.62 743.67 12935.19 32221.56 60149.87 133895.41 ▇▅▃▁▁
2067 0 1 37104.21 27465.93 757.06 13246.33 32784.38 60789.99 132699.46 ▇▅▅▁▁
2068 0 1 37490.45 27485.71 770.69 13564.09 33350.52 61426.18 131547.00 ▇▅▅▁▁
2069 0 1 37878.32 27504.84 784.56 13888.51 33919.77 62058.27 130436.05 ▇▅▅▁▁
2070 0 1 38267.76 27523.21 798.68 14219.65 34491.95 62645.76 129364.72 ▇▆▅▁▁
2071 0 1 38658.70 27540.72 813.06 14557.57 35066.85 63169.53 128331.22 ▇▆▅▂▁
2072 0 1 39051.10 27557.26 827.69 14902.30 35644.28 63688.11 127333.90 ▇▆▅▂▁
2073 0 1 39444.87 27572.74 842.59 15253.90 36224.03 64201.42 126371.15 ▇▆▅▂▁
2074 0 1 39839.97 27587.06 857.76 15612.39 36805.89 64709.40 125441.49 ▇▆▅▂▁
2075 0 1 40236.32 27600.15 873.20 15977.82 37389.66 65211.97 124543.50 ▇▇▆▂▁
2076 0 1 40633.86 27611.92 888.92 16350.20 37975.13 65709.08 123675.84 ▇▇▆▂▁
2077 0 1 41032.55 27622.29 904.92 16729.56 38562.09 66200.68 122837.23 ▇▇▆▃▁
2078 0 1 41432.31 27631.20 921.20 17115.91 39160.71 66686.69 122026.48 ▇▆▆▃▁
2079 0 1 41833.08 27638.57 937.79 17509.27 39773.01 67167.10 121242.45 ▇▆▆▃▁
2080 0 1 42234.80 27644.35 954.67 17909.65 40397.15 67641.84 120484.05 ▇▆▅▃▁
2081 0 1 42637.41 27648.47 971.85 18317.03 41039.65 68110.88 119750.25 ▇▆▅▃▁
2082 0 1 43040.85 27650.88 989.34 18731.41 41684.07 68574.20 119040.08 ▇▆▅▅▁
2083 0 1 43445.06 27651.52 1007.15 19152.79 42330.19 69031.76 118352.60 ▇▆▅▅▁
2084 0 1 43849.98 27650.34 1025.28 19581.13 42977.76 69483.54 117686.94 ▇▆▅▅▁
2085 0 1 44255.54 27647.31 1043.74 20016.42 43626.56 69929.52 117042.26 ▇▆▅▅▁
2086 0 1 44661.69 27642.36 1062.52 20458.63 44276.35 70369.68 116417.74 ▇▆▅▆▁
2087 0 1 45068.37 27635.46 1081.65 20907.70 44926.90 70804.02 115812.64 ▇▆▆▆▁
2088 0 1 45475.51 27626.57 1101.12 21363.61 45577.99 71232.52 115226.22 ▇▇▆▆▁
2089 0 1 45883.06 27615.65 1120.94 21826.29 46229.37 71655.18 114657.79 ▇▇▇▇▁
2090 0 1 46290.96 27602.67 1141.11 22295.68 46880.81 72072.01 114106.68 ▇▇▇▇▁
2091 0 1 46699.15 27587.59 1161.65 22771.73 47532.10 72477.70 113572.27 ▇▇▇▇▁
2092 0 1 47107.57 27570.38 1182.56 23254.35 48182.99 72862.40 113053.96 ▇▇▇▇▁
2093 0 1 47516.16 27551.01 1203.85 23743.47 48833.27 73241.40 112551.15 ▇▇▇▇▁
2094 0 1 47924.86 27529.46 1225.52 24239.00 49482.71 73614.73 112063.31 ▇▇▇▇▁
2095 0 1 48333.62 27505.71 1247.58 24740.84 50131.09 73982.40 111589.91 ▇▇▇▇▁
2096 0 1 48742.37 27479.72 1270.04 25248.90 50778.20 74344.45 111130.43 ▇▆▇▇▂
2097 0 1 49151.07 27451.48 1292.90 25763.07 51423.81 74700.90 110684.40 ▇▆▇▇▂
2098 0 1 49559.65 27420.97 1316.17 26283.23 52067.72 75051.78 110251.36 ▇▆▇▇▂
2099 0 1 49968.05 27388.16 1339.86 26809.26 52709.72 75397.13 109830.86 ▆▆▇▇▂
2100 0 1 50376.22 27353.05 1363.98 27341.03 53349.60 75736.97 109422.48 ▆▆▇▇▂

cat(“HDI: Number of empty records removed:”, sum(is.na(hdi_clean))) hdi_clean <- hdi_clean %>% drop_na() cat(“Broadband: Number of empty records removed:”, sum(is.na(broadband_clean))) broadband_clean <- broadband_clean %>% drop_na() cat(“GDP: Number of empty records removed:”, sum(is.na(gdp_clean))) gdp_clean <- gdp_clean %>% drop_na()

#create a long table for easy analysis
broadband_long <- broadband_clean %>%
  pivot_longer(cols=-country, names_to="year", values_to="broadband") %>%
  mutate(year=as.numeric(year))
hdi_long <- hdi_clean %>%
  pivot_longer(cols=-country, names_to="year", values_to="hdi") %>%
  mutate(year=as.numeric(year))
gdp_long <- gdp_clean[-1.] %>%
  rename(country=name) %>%
  pivot_longer(cols=-country, names_to="year", values_to="gdp") %>%
  mutate(year=as.numeric(year))
#Join the HDI & Broadband data by year and country
merged_data <- merge(broadband_long, hdi_long, by = c("country","year"))

#Join the GDP dataset
merged_data <- merge(merged_data, gdp_long, by = c("country", "year"))

#remove the missing values
merged_data <- na.omit(merged_data)

Data Visualization

#visualize the relationship between broadband subscription and HDI over the time
ggplot(merged_data, aes(x = broadband, y = hdi)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  geom_smooth(method = "lm", color = "darkred") +
  theme_minimal() +
  labs(title = "Relationship between Broadband Subscriptions and HDI",
       x = "Broadband subscriptions per 100 people",
       y = "Human Development Index")

#Looking at the first research question #What is the relationship between HDI and broadband subscriptions, based on the latest available data, which is 2023.

#Pull the data for 2023 only
data_2023 <- merged_data %>% filter(year==2023)

#visualize the relationship between broadband subscription and HDI in the year of 2023
ggplot(data_2023, aes(x = broadband, y = hdi)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  geom_smooth(method = "lm", color = "darkred") +
  theme_minimal() +
  labs(title = "Relationship between Broadband Subscriptions and HDI in the year 2023",
       x = "Broadband subscriptions per 100 people",
       y = "Human Development Index")

#Test the skewness and distribution of the data
#Histogram of HDI in 2023. Histogram show that the HDI of 2023 is mildy left skewed
ggplot(data_2023,aes(x=hdi))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of HDI in 2023",
       x = "HDI",
       y = "Frequency")

# Box plot of HDI in 2023
ggplot(data_2023, aes(y=hdi)) + 
  geom_boxplot() + 
  labs(title = "Box plot of HDI in 2023",
       y="Count")

#Histogram of broadband in 2023. Histogram show that the broadband data is right skewed
ggplot(data_2023,aes(x=broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Broadband in 2023",
       x = "Broadband",
       y = "Frequency")

# Box plot of broadband in 2023
ggplot(data_2023, aes(y=broadband)) + 
  geom_boxplot() + 
  labs(title = "Box plot of Broadband in 2023",
       y="Count")

Need to transform the broadband dataset. Option 1: Log Transform

#log transformed the broadband
data_2023 <- data_2023 %>%
  mutate(broadband = ifelse(broadband == 0, NA, broadband)) %>%
  mutate(log_broadband = log(broadband))

#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=log_broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Log Tranformed Broadband in 2023",
       x = "Log Transformed Broadband",
       y = "Frequency")

shapiro.test(data_2023$log_broadband)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2023$log_broadband
## W = 0.8112, p-value = 1.241e-12
qqnorm(data_2023$log_broadband, main = "Q-Q Plot for log transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")

# Add a reference line
qqline(data_2023$log_broadband, col = "steelblue", lwd = 2)

Option 2: Square root Transform

#Square root transform of the broadband dataset. 
data_2023 <- data_2023 %>%
  mutate(sqrt_broadband = sqrt(broadband))

#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=sqrt_broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Square rootTranformed Broadband in 2023",
       x = "Square root Transformed Broadband",
       y = "Frequency")

shapiro.test(data_2023$sqrt_broadband)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2023$sqrt_broadband
## W = 0.9317, p-value = 1.306e-06
qqnorm(data_2023$sqrt_broadband, main = "Q-Q Plot for Square root transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")

# Add a reference line
qqline(data_2023$sqrt_broadband, col = "steelblue", lwd = 2)

#Create a scatter plot of square root broadband vs hdi of 2023
ggplot(data_2023, aes(x = sqrt_broadband, y = hdi)) +
  geom_point(color = "steelblue") +                                               # Add scatter points
  geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
  labs(title = "Scatter Plot with Line of Best Fit of Square root Broadband vs HDI",
       x = "Square root Broadband Subscription per 100",
       y = "HDI")

#Create a model for 2023 data
model_2023 <- lm(hdi ~ sqrt_broadband, data=data_2023)
summary(model_2023)
## 
## Call:
## lm(formula = hdi ~ sqrt_broadband, data = data_2023)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.184558 -0.036556  0.002638  0.036693  0.254059 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.536983   0.010067   53.34   <2e-16 ***
## sqrt_broadband 0.060655   0.002339   25.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06115 on 148 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.8197, Adjusted R-squared:  0.8184 
## F-statistic: 672.7 on 1 and 148 DF,  p-value: < 2.2e-16

#We want to do a comparision between 2011 and 2023. #In 2011, the United Nations General Assembly highlighed Internet access as a basic human right

data_2011 <- merged_data %>% filter(year==2011)

#visualize the relationship between broadband subscription and HDI in the year of 2011
ggplot(data_2011, aes(x = broadband, y = hdi)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  geom_smooth(method = "lm", color = "darkred") +
  theme_minimal() +
  labs(title = "Relationship between Broadband Subscriptions and HDI in the year 2011",
       x = "Broadband subscriptions per 100 people",
       y = "Human Development Index")
## `geom_smooth()` using formula = 'y ~ x'

#Test the skewness and distribution of the data
#Histogram of HDI in 2011. Histogram show that the HDI of 2011 is mildy left skewed
ggplot(data_2011,aes(x=hdi))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of HDI in 2011",
       x = "HDI",
       y = "Frequency")

# Box plot of HDI in 2011
ggplot(data_2011, aes(y=hdi)) + 
  geom_boxplot() + 
  labs(title = "Box plot of HDI in 2011",
       y="Count")

#Histogram of broadband in 2011. Histogram show that the broadband data is severe right skewed
ggplot(data_2011,aes(x=broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Broadband in 2011",
       x = "Broadband",
       y = "Frequency")

# Box plot of broadband in 2023
ggplot(data_2011, aes(y=broadband)) + 
  geom_boxplot() + 
  labs(title = "Box plot of Broadband in 2011",
       y="Count")

Need to transform the braodband dataset. Option 1: Log Transform

data_2011 <- data_2011 %>%
  filter(broadband != 0) %>%
  mutate(log_broadband = log(broadband))

#Testing the skewness of log transform broadband values
ggplot(data_2011,aes(x=log_broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Log Tranformed Broadband in 2011",
       x = "Log Transformed Broadband",
       y = "Frequency")

shapiro.test(data_2011$log_broadband)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2011$log_broadband
## W = 0.90542, p-value = 3.125e-09
qqnorm(data_2011$log_broadband, main = "Q-Q Plot for log transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")

# Add a reference line
qqline(data_2011$log_broadband, col = "steelblue", lwd = 2)

Option 2: Square root Transform

#Square root transform the broadband dataset. 
data_2011 <- data_2011 %>%
  mutate(sqrt_broadband = sqrt(broadband))

#Testing the skewness of log transform broadband values
ggplot(data_2011,aes(x=sqrt_broadband))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Square root Tranformed Broadband in 2011",
       x = "Square root Transformed Broadband",
       y = "Frequency")

shapiro.test(data_2011$sqrt_broadband)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2011$sqrt_broadband
## W = 0.91094, p-value = 7.037e-09
qqnorm(data_2011$sqrt_broadband, main = "Q-Q Plot for Square root transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")

# Add a reference line
qqline(data_2011$sqrt_broadband, col = "steelblue", lwd = 2)

#Create a scatter plot of square root broadband vs hdi of 2011
ggplot(data_2011, aes(x = sqrt_broadband, y = hdi)) +
  geom_point(color = "steelblue") +                                               # Add scatter points
  geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
  labs(title = "Scatter Plot with Line of Best Fit of Square root Broadband vs HDI",
       x = "Square root Broadband Subscription per 100",
       y = "HDI")

#Create a model for 2011 data
model_2011 <- lm(hdi ~ sqrt_broadband, data=data_2011)
summary(model_2011)
## 
## Call:
## lm(formula = hdi ~ sqrt_broadband, data = data_2011)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.201315 -0.038654 -0.004344  0.044943  0.227565 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.539498   0.008574   62.92   <2e-16 ***
## sqrt_broadband 0.070482   0.002766   25.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07026 on 175 degrees of freedom
## Multiple R-squared:  0.7877, Adjusted R-squared:  0.7865 
## F-statistic: 649.4 on 1 and 175 DF,  p-value: < 2.2e-16

#Question 2: Finding the outliers countries in 2023

data_2023 <- na.omit(data_2023) %>%
  mutate(
    fitted_hdi = fitted(model_2023,),
    residuals = resid(model_2023),
    std_resid = rstandard(model_2023)
  )

outliers <- data_2023 %>%
  filter(abs(std_resid) > 3)

outliers
##   country year broadband   hdi       gdp log_broadband sqrt_broadband
## 1  Kuwait 2023     1.010 0.852 46714.534   0.009950331      1.0049876
## 2 Somalia 2023     0.723 0.404  1129.263  -0.324346057      0.8502941
##   fitted_hdi  residuals std_resid
## 1  0.5979406  0.2540594   4.19170
## 2  0.5885576 -0.1845576  -3.04697

#Question 3: looking at GDP as the controlling factor #Testing the skewness of GDP

#histogram of GDP in 2023
ggplot(data_2023,aes(x=gdp))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of GDP in 2023",
       x = "GDP",
       y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

# Box plot of GDP in 2023
ggplot(data_2023, aes(y=gdp)) + 
  geom_boxplot() + 
  labs(title = "Box plot of GDP in 2023",
       y="Count")

The GDP dataset is right skewed and need to be transformed. Option 1: Log transform

data_2023 <- data_2023 %>%
  filter(broadband != 0) %>%
  mutate(log_gdp = log(gdp))

#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=log_gdp))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Log Tranformed GDP in 2023",
       x = "Log Transformed GDP",
       y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

shapiro.test(data_2023$log_gdp)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2023$log_gdp
## W = 0.96318, p-value = 0.0004862
qqnorm(data_2023$log_gdp, main = "Q-Q Plot for log transformed GDP", xlab = "Theoretical Quantiles", ylab = "log transformed GDP")

# Add a reference line
qqline(data_2023$log_gdp, col = "steelblue", lwd = 2)

Option 2: Square root transform gdp

data_2023 <- data_2023 %>%
  filter(broadband != 0) %>%
  mutate(sqrt_gdp = sqrt(gdp))

#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=sqrt_gdp))+
  geom_histogram(colour="white", fill="steelblue")+
  labs(title="Histogram of Square root Tranformed GDP in 2023",
       x = "Square root Transformed GDP",
       y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

shapiro.test(data_2023$sqrt_gdp)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_2023$sqrt_gdp
## W = 0.96378, p-value = 0.0005542
qqnorm(data_2023$sqrt_gdp, main = "Q-Q Plot for Square root transformed GDP", xlab = "Theoretical Quantiles", ylab = "Square root transformed GDP")

# Add a reference line
qqline(data_2023$sqrt_gdp, col = "steelblue", lwd = 2)

#Create a scatter plot of log GDP vs hdi of 2023

ggplot(data_2023, aes(x = log_gdp, y = hdi)) +
  geom_point(color = "steelblue") +                                               # Add scatter points
  geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
  labs(title = "Scatter Plot with Line of Best Fit of Log GDP vs HDI",
       x = "Log GDP",
       y = "HDI")
## `geom_smooth()` using formula = 'y ~ x'

#Create a multi regression with gdp as the control variable

multi_reg <- lm (hdi ~ sqrt_broadband + log_gdp, data = data_2023)

summary(multi_reg)
## 
## Call:
## lm(formula = hdi ~ sqrt_broadband + log_gdp, data = data_2023)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.116794 -0.016716  0.005263  0.018851  0.115077 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.147792   0.037583  -3.932 0.000129 ***
## sqrt_broadband  0.019361   0.002586   7.486 6.02e-12 ***
## log_gdp         0.087961   0.004775  18.422  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03373 on 147 degrees of freedom
## Multiple R-squared:  0.9455, Adjusted R-squared:  0.9448 
## F-statistic:  1275 on 2 and 147 DF,  p-value: < 2.2e-16

#Checking assumptions

plot(multi_reg, which = 1)

dwtest(multi_reg)
## 
##  Durbin-Watson test
## 
## data:  multi_reg
## DW = 1.8992, p-value = 0.2702
## alternative hypothesis: true autocorrelation is greater than 0
res_model <- residuals(multi_reg)
shapiro.test(res_model)
## 
##  Shapiro-Wilk normality test
## 
## data:  res_model
## W = 0.97612, p-value = 0.01025
plot(multi_reg, which = 2)

pred_model <- fitted(multi_reg)
plot(pred_model, res_model,
     xlab = "Predicted Values (Fitted)",
     ylab = "Residuals",
     main = "Residuals vs Predicted Values",
     col = "steelblue")
abline(h = 0, col = "red", lwd = 2)

vif(multi_reg)
## sqrt_broadband        log_gdp 
##       4.019114       4.019114